Operational Considerations for Streaming Media
draft-ietf-mops-streaming-opcons-05
MOPS J. Holland
Internet-Draft Akamai Technologies, Inc.
Intended status: Informational A. Begen
Expires: 10 December 2021 Networked Media
S. Dawkins
Tencent America LLC
8 June 2021
Operational Considerations for Streaming Media
draft-ietf-mops-streaming-opcons-05
Abstract
This document provides an overview of operational networking issues
that pertain to quality of experience in streaming of video and other
high-bitrate media over the Internet.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
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time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 10 December 2021.
Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Notes for Contributors and Reviewers . . . . . . . . . . 4
1.1.1. Venues for Contribution and Discussion . . . . . . . 4
1.1.2. Template for Contributions . . . . . . . . . . . . . 5
1.1.3. History of Public Discussion . . . . . . . . . . . . 6
2. Bandwidth Provisioning . . . . . . . . . . . . . . . . . . . 6
2.1. Scaling Requirements for Media Delivery . . . . . . . . . 6
2.1.1. Video Bitrates . . . . . . . . . . . . . . . . . . . 6
2.1.2. Virtual Reality Bitrates . . . . . . . . . . . . . . 7
2.2. Path Requirements . . . . . . . . . . . . . . . . . . . . 8
2.3. Caching Systems . . . . . . . . . . . . . . . . . . . . . 8
2.4. Predictable Usage Profiles . . . . . . . . . . . . . . . 9
2.5. Unpredictable Usage Profiles . . . . . . . . . . . . . . 9
2.6. Extremely Unpredictable Usage Profiles . . . . . . . . . 10
3. Latency Considerations . . . . . . . . . . . . . . . . . . . 12
3.1. Ultra Low-Latency . . . . . . . . . . . . . . . . . . . . 12
3.2. Low-Latency Live . . . . . . . . . . . . . . . . . . . . 13
3.3. Non-Low-Latency Live . . . . . . . . . . . . . . . . . . 14
3.4. On-Demand . . . . . . . . . . . . . . . . . . . . . . . . 15
4. Adaptive Encoding, Adaptive Delivery, and Measurement
Collection . . . . . . . . . . . . . . . . . . . . . . . 15
4.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2. Adaptive Encoding . . . . . . . . . . . . . . . . . . . . 16
4.3. Adaptive Segmented Delivery . . . . . . . . . . . . . . . 16
4.3.1. Idle Time between Segments . . . . . . . . . . . . . 17
4.3.2. Head-of-Line Blocking . . . . . . . . . . . . . . . . 17
4.4. Measurement Collection . . . . . . . . . . . . . . . . . 18
4.4.1. CTA-2066: Streaming Quality of Experience Events,
Properties and Metrics . . . . . . . . . . . . . . . 18
4.4.2. CTA-5004: Common Media Client Data (CMCD) . . . . . . 19
4.5. Unreliable Transport . . . . . . . . . . . . . . . . . . 19
5. Evolution of Transport Protocols and Transport Protocol
Behaviors . . . . . . . . . . . . . . . . . . . . . . . . 20
5.1. UDP and Its Behavior . . . . . . . . . . . . . . . . . . 20
5.2. TCP and Its Behavior . . . . . . . . . . . . . . . . . . 21
5.3. The QUIC Protocol and Its Behavior . . . . . . . . . . . 22
6. Streaming Encrypted Media . . . . . . . . . . . . . . . . . . 24
6.1. General Considerations for Media Encryption . . . . . . . 25
6.2. Considerations for "Hop-by-Hop" Media Encryption . . . . 26
6.3. Considerations for "End-to-End" Media Encryption . . . . 27
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 28
8. Security Considerations . . . . . . . . . . . . . . . . . . . 28
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 28
10. Informative References . . . . . . . . . . . . . . . . . . . 28
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 35
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1. Introduction
As the internet has grown, an increasingly large share of the traffic
delivered to end users has become video. Estimates put the total
share of internet video traffic at 75% in 2019, expected to grow to
82% by 2022. This estimate projects the gross volume of video
traffic will more than double during this time, based on a compound
annual growth rate continuing at 34% (from Appendix D of [CVNI]).
A substantial part of this growth is due to increased use of
streaming video, although the amount of video traffic in real-time
communications (for example, online videoconferencing) has also grown
significantly. While both streaming video and videoconferencing have
real-time delivery and latency requirements, these requirements vary
from one application to another. For example, videoconferencing
demands an end-to-end (one-way) latency of a few hundreds of
milliseconds whereas live streaming can tolerate latencies of several
seconds.
This document specifically focuses on the streaming applications and
defines streaming as follows:
* Streaming is transmission of a continuous media from a server to a
client and its simultaneous consumption by the client.
* Here, continuous media refers to media and associated streams such
as video, audio, metadata, etc. In this definition, the critical
term is "simultaneous", as it is not considered streaming if one
downloads a video file and plays it after the download is
completed, which would be called download-and-play.
This has two implications.
* First, the server's transmission rate must (loosely or tightly)
match to client's consumption rate in order to provide
uninterrupted playback. That is, the client must not run out of
data (buffer underrun) or accept more data than it can buffer
before playback (buffer overrun) as any excess media is simply
discarded.
* Second, the client's consumption rate is limited not only by
bandwidth availability but also real-time constraints. That is,
the client cannot fetch media that is not available from a server
yet.
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In many contexts, video traffic can be handled transparently as
generic application-level traffic. However, as the volume of video
traffic continues to grow, it's becoming increasingly important to
consider the effects of network design decisions on application-level
performance, with considerations for the impact on video delivery.
This document aims to provide a taxonomy of networking issues as they
relate to quality of experience in internet video delivery. The
focus is on capturing characteristics of video delivery that have
surprised network designers or transport experts without specific
video expertise, since these highlight key differences between common
assumptions in existing networking documents and observations of
video delivery issues in practice.
Making specific recommendations for mitigating these issues is out of
scope, though some existing mitigations are mentioned in passing.
The intent is to provide a point of reference for future solution
proposals to use in describing how new technologies address or avoid
these existing observed problems.
1.1. Notes for Contributors and Reviewers
Note to RFC Editor: Please remove this section and its subsections
before publication.
This section is to provide references to make it easier to review the
development and discussion on the draft so far.
1.1.1. Venues for Contribution and Discussion
This document is in the Github repository at:
https://github.com/ietf-wg-mops/draft-ietf-mops-streaming-opcons
(https://github.com/ietf-wg-mops/draft-ietf-mops-streaming-opcons)
Readers are welcome to open issues and send pull requests for this
document.
Substantial discussion of this document should take place on the MOPS
working group mailing list (mops@ietf.org).
* Join: https://www.ietf.org/mailman/listinfo/mops
(https://www.ietf.org/mailman/listinfo/mops)
* Search: https://mailarchive.ietf.org/arch/browse/mops/
(https://mailarchive.ietf.org/arch/browse/mops/)
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1.1.2. Template for Contributions
Contributions are solicited regarding issues and considerations that
have an impact on media streaming operations.
Please note that contributions may be merged and substantially
edited, and as a reminder, please carefully consider the Note Well
before contributing: https://datatracker.ietf.org/submit/note-well/
(https://datatracker.ietf.org/submit/note-well/)
Contributions can be emailed to mops@ietf.org, submitted as issues to
the issue tracker of the repository in Section 1.1.1, or emailed to
the document authors at draft-ietf-mops-streaming-opcons@ietf.org.
Contributors describing an issue not yet addressed in the draft are
requested to provide the following information, where applicable:
* a suggested title or name for the issue
* a long-term pointer to the best reference describing the issue
* a short description of the nature of the issue and its impact on
media quality of service, including:
- where in the network this issue has root causes
- who can detect this issue when it occurs
* an overview of the issue's known prevalence in practice. pointers
to write-ups of high-profile incidents are a plus.
* a list of known mitigation techniques, with (for each known
mitigation):
- a name for the mitigation technique
- a long-term pointer to the best reference describing it
- a short description of the technique:
o what it does
o where in the network it operates
o an overview of the tradeoffs involved-how and why it's
helpful, what it costs.
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- supplemental information about the technique's deployment
prevalence and status
1.1.3. History of Public Discussion
Presentations:
* IETF 105 BOF:
https://www.youtube.com/watch?v=4G3YBVmn9Eo&t=47m21s
(https://www.youtube.com/watch?v=4G3YBVmn9Eo&t=47m21s)
* IETF 106 meeting:
https://www.youtube.com/watch?v=4_k340xT2jM&t=7m23s
(https://www.youtube.com/watch?v=4_k340xT2jM&t=7m23s)
* MOPS Interim Meeting 2020-04-15:
https://www.youtube.com/watch?v=QExiajdC0IY&t=10m25s
(https://www.youtube.com/watch?v=QExiajdC0IY&t=10m25s)
* IETF 108 meeting:
https://www.youtube.com/watch?v=ZaRsk0y3O9k&t=2m48s
(https://www.youtube.com/watch?v=ZaRsk0y3O9k&t=2m48s)
* MOPS 2020-10-30 Interim meeting:
https://www.youtube.com/watch?v=vDZKspv4LXw&t=17m15s
(https://www.youtube.com/watch?v=vDZKspv4LXw&t=17m15s)
2. Bandwidth Provisioning
2.1. Scaling Requirements for Media Delivery
2.1.1. Video Bitrates
Video bitrate selection depends on many variables including the
resolution (height and width), frame rate, color depth, codec,
encoding parameters, scene complexity and amount of motion.
Generally speaking, as the resolution, frame rate, color depth, scene
complexity and amount of motion increase, the encoding bitrate
increases. As newer codecs with better compression tools are used,
the encoding bitrate decreases. Similarly, a multi-pass encoding
generally produces better quality output compared to single-pass
encoding at the same bitrate, or delivers the same quality at a lower
bitrate.
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Here are a few common resolutions used for video content, with
typical ranges of bitrate for the two most popular video codecs
[Encodings].
+============+================+============+============+
| Name | Width x Height | AVC | HEVC |
+============+================+============+============+
| DVD | 720 x 480 | 1.0 Mbps | 0.5 Mbps |
+------------+----------------+------------+------------+
| 720p (1K) | 1280 x 720 | 3-4.5 Mbps | 2-4 Mbps |
+------------+----------------+------------+------------+
| 1080p (2K) | 1920 x 1080 | 6-8 Mbps | 4.5-7 Mbps |
+------------+----------------+------------+------------+
| 2160p (4k) | 3840 x 2160 | N/A | 10-20 Mbps |
+------------+----------------+------------+------------+
Table 1
2.1.2. Virtual Reality Bitrates
The bitrates given in Section 2.1.1 describe video streams that
provide the user with a single, fixed, point of view - so, the user
has no "degrees of freedom", and the user sees all of the video image
that is available.
Even basic virtual reality (360-degree) videos that allow users to
look around freely (referred to as "three degrees of freedom", or
3DoF) require substantially larger bitrates when they are captured
and encoded as such videos require multiple fields of view of the
scene. The typical multiplication factor is 8 to 10. Yet, due to
smart delivery methods such as viewport-based or tiled-based
streaming, we do not need to send the whole scene to the user.
Instead, the user needs only the portion corresponding to its
viewpoint at any given time.
In more immersive applications, where limited user movement ("three
degrees of freedom plus", or 3DoF+) or full user movement ("six
degrees of freedom", or 6DoF) is allowed, the required bitrate grows
even further. In this case, immersive content is typically referred
to as volumetric media. One way to represent the volumetric media is
to use point clouds, where streaming a single object may easily
require a bitrate of 30 Mbps or higher. Refer to [MPEGI] and [PCC]
for more details.
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2.2. Path Requirements
The bitrate requirements in Section 2.1 are per end-user actively
consuming a media feed, so in the worst case, the bitrate demands can
be multiplied by the number of simultaneous users to find the
bandwidth requirements for a router on the delivery path with that
number of users downstream. For example, at a node with 10,000
downstream users simultaneously consuming video streams,
approximately 80 Gbps might be necessary in order for all of them to
get typical content at 1080p resolution.
However, when there is some overlap in the feeds being consumed by
end users, it is sometimes possible to reduce the bandwidth
provisioning requirements for the network by performing some kind of
replication within the network. This can be achieved via object
caching with delivery of replicated objects over individual
connections, and/or by packet-level replication using multicast.
To the extent that replication of popular content can be performed,
bandwidth requirements at peering or ingest points can be reduced to
as low as a per-feed requirement instead of a per-user requirement.
2.3. Caching Systems
When demand for content is relatively predictable, and especially
when that content is relatively static, caching content close to
requesters, and pre-loading caches to respond quickly to initial
requests is often useful (for example, HTTP/1.1 caching is described
in [RFC7234]). This is subject to the usual considerations for
caching - for example, how much data must be cached to make a
significant difference to the requester, and how the benefits of
caching and pre-loading caches balances against the costs of tracking
"stale" content in caches and refreshing that content.
It is worth noting that not all high-demand content is "live"
content. One popular example is when popular streaming content can
be staged close to a significant number of requesters, as can happen
when a new episode of a popular show is released. This content may
be largely stable, so low-cost to maintain in multiple places
throughout the Internet. This can reduce demands for high end-to-end
bandwidth without having to use mechanisms like multicast.
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Caching and pre-loading can also reduce exposure to peering point
congestion, since less traffic crosses the peering point exchanges if
the caches are placed in peer networks, especially when the content
can be pre-loaded during off-peak hours, and especially if the
transfer can make use of "Lower-Effort Per-Hop Behavior (LE PHB) for
Differentiated Services" [RFC8622], "Low Extra Delay Background
Transport (LEDBAT)" [RFC6817], or similar mechanisms.
All of this depends, of course, on the ability of a content provider
to predict usage and provision bandwidth, caching, and other
mechanisms to meet the needs of users. In some cases (Section 2.4),
this is relatively routine, but in other cases, it is more difficult
(Section 2.5, Section 2.6).
2.4. Predictable Usage Profiles
Historical data shows that users consume more video and videos at
higher bitrates than they did in the past on their connected devices.
Improvements in the codecs that help with reducing the encoding
bitrates with better compression algorithms could not have offset the
increase in the demand for the higher quality video (higher
resolution, higher frame rate, better color gamut, better dynamic
range, etc.). In particular, mobile data usage has shown a large
jump over the years due to increased consumption of entertainment as
well as conversational video.
TBD: insert charts showing historical relative data usage patterns
with error bars by time of day in consumer networks?
TBD: Cross-ref vs. video quality by time of day in practice for some
case study? Not sure if there's a good way to capture a generalized
insight here, but it seems worth making the point that demand
projections can be used to help with e.g. power consumption with
routing architectures that provide for modular scalability.
2.5. Unpredictable Usage Profiles
Although TCP/IP has been used with a number of widely used
applications that have symmetric bandwidth requirements (similar
bandwidth requirements in each direction between endpoints), many
widely-used Internet applications operate in client-server roles,
with asymmetric bandwidth requirements. A common example might be an
HTTP GET operation, where a client sends a relatively small HTTP GET
request for a resource to an HTTP server, and often receives a
significantly larger response carrying the requested resource. When
HTTP is commonly used to stream movie-length video, the ratio between
response size and request size can become arbitrarily large.
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For this reason, operators may pay more attention to downstream
bandwidth utilization when planning and managing capacity. In
addition, operators have been able to deploy access networks for end
users using underlying technologies that are inherently asymmetric,
favoring downstream bandwidth (e.g. ADSL, cellular technologies,
most IEEE 802.11 variants), assuming that users will need less
upstream bandwidth than downstream bandwidth. This strategy usually
works, except when it faiis because application bandwidth usage
patterns have changed in ways that were not predicted.
One example of this type of change was when peer-to-peer file sharing
applications gained popularity in the early 2000s. To take one well-
documented case ([RFC5594]), the Bittorrent application created
"swarms" of hosts, uploading and downloading files to each other,
rather than communicating with a server. Bittorrent favored peers
who uploaded as much as they downloaded, so that new Bittorrent users
had an incentive to significantly increase their upstream bandwidth
utilization.
The combination of the large volume of "torrents" and the peer-to-
peer characteristic of swarm transfers meant that end user hosts were
suddenly uploading higher volumes of traffic to more destinations
than was the case before Bittorrent. This caused at least one large
ISP to attempt to "throttle" these transfers, to mitigate the load
that these hosts placed on their network. These efforts were met by
increased use of encryption in Bittorrent, similar to an arms race,
and set off discussions about "Net Neutrality" and calls for
regulatory action.
Especially as end users increase use of video-based social networking
applications, it will be helpful for access network providers to
watch for increasing numbers of end users uploading significant
amounts of content.
2.6. Extremely Unpredictable Usage Profiles
The causes of unpredictable usage described in Section 2.5 were more
or less the result of human choices, but we were reminded during a
post-IETF 107 meeting that humans are not always in control, and
forces of nature can cause enormous fluctuations in traffic patterns.
In his talk, Sanjay Mishra [Mishra] reported that after the CoViD-19
pandemic broke out in early 2020,
* Comcast's streaming and web video consumption rose by 38%, with
their reported peak traffic up 32% overall between March 1 to
March 30,
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* AT&T reported a 28% jump in core network traffic (single day in
April, as compared to pre stay-at-home daily average traffic),
with video accounting for nearly half of all mobile network
traffic, while social networking and web browsing remained the
highest percentage (almost a quarter each) of overall mobility
traffic, and
* Verizon reported similar trends with video traffic up 36% over an
average day (pre COVID-19)}.
We note that other operators saw similar spikes during this time
period. Craig Labowitz [Labovitz] reported
* Weekday peak traffic increases over 45%-50% from pre-lockdown
levels,
* A 30% increase in upstream traffic over their pre-pandemic levels,
and
* A steady increase in the overall volume of DDoS traffic, with
amounts exceeding the pre-pandemic levels by 40%. (He attributed
this increase to the significant rise in gaming-related DDoS
attacks ([LabovitzDDoS]), as gaming usage also increased.)
Subsequently, the Internet Architecture Board (IAB) held a COVID-19
Network Impacts Workshop [IABcovid] in November 2020. Given a larger
number of reports and more time to reflect, the following
observations from the draft workshop report are worth considering.
* Participants describing different types of networks reported
different kinds of impacts, but all types of networks saw impacts.
* Mobile networks saw traffic reductions and residential networks
saw significant increases.
* Reported traffic increases from ISPs and IXPs over just a few
weeks were as big as the traffic growth over the course of a
typical year, representing a 15-20% surge in growth to land at a
new normal that was much higher than anticipated.
* At DE-CIX Frankfurt, the world's largest Internet Exchange Point
in terms of data throughput, the year 2020 has seen the largest
increase in peak traffic within a single year since the IXP was
founded in 1995.
* The usage pattern changed significantly as work-from-home and
videoconferencing usage peaked during normal work hours, which
would have typically been off-peak hours with adults at work and
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children at school. One might expect that the peak would have had
more impact on networks if it had happened during typical evening
peak hours for video streaming applications.
* The increase in daytime bandwidth consumption reflected both
significant increases in "essential" applications such as
videoconferencing and VPNs, and entertainment applications as
people watched videos or played games.
* At the IXP-level, it was observed that port utilization increased.
This phenomenon is mostly explained by a higher traffic demand
from residential users.
3. Latency Considerations
Streaming media latency refers to the "glass-to-glass" time duration,
which is the delay between the real-life occurrence of an event and
the streamed media being appropriately displayed on an end user's
device. Note that this is different from the network latency
(defined as the time for a packet to cross a network from one end to
another end) because it includes video encoding/decoding and
buffering time, and for most cases also ingest to an intermediate
service such as a CDN or other video distribution service, rather
than a direct connection to an end user.
Streaming media can be usefully categorized according to the
application's latency requirements into a few rough categories:
* ultra low-latency (less than 1 second)
* low-latency live (less than 10 seconds)
* non-low-latency live (10 seconds to a few minutes)
* on-demand (hours or more)
3.1. Ultra Low-Latency
Ultra low-latency delivery of media is defined here as having a
glass-to-glass delay target under one second.
This level of latency is sometimes necessary for real-time
interactive applications such as video conferencing, operation of
remote control devices or vehicles, or remotely hosted real-time
gaming systems. Some media content providers aim to achieve this
level of latency for live media events involving sports, but have
usually so far been unsuccessful over the internet at scale, though
it is often possible within a localized environment with a controlled
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network, such as inside a specific venue connected to the event.
Applications operating in this domain that encounter transient
network events such as loss or reordering of some packets often
experience user-visible artifacts in the media.
Applications requiring ultra low latency for media delivery are
usually tightly constrained on the available choices for media
transport technologies, and sometimes may need to operate in
controlled environments to reliably achieve their latency and quality
goals.
Most applications operating over IP networks and requiring latency
this low use the Real-time Transport Protocol (RTP) [RFC3550] or
WebRTC [RFC8825], which uses RTP for the media transport as well as
several other protocols necessary for safe operation in browsers.
Worth noting is that many applications for ultra low-latency delivery
do not need to scale to more than one user at a time, which
simplifies many delivery considerations relative to other use cases.
For applications that need to replicate streams to multiple users,
especially at a scale exceeding tens of users, this level of latency
has historically been nearly impossible to achieve except with the
use of multicast or planned provisioning in controlled networks.
Recommended reading for applications adopting an RTP-based approach
also includes [RFC7656]. For increasing the robustness of the
playback by implementing adaptive playout methods, refer to [RFC4733]
and [RFC6843].
Applications with further-specialized latency requirements are out of
scope for this document.
3.2. Low-Latency Live
Low-latency live delivery of media is defined here as having a glass-
to-glass delay target under 10 seconds.
This level of latency is targeted to have a user experience similar
to traditional broadcast TV delivery. A frequently cited problem
with failing to achieve this level of latency for live sporting
events is the user experience failure from having crowds within
earshot of one another who react audibly to an important play, or
from users who learn of an event in the match via some other channel,
for example social media, before it has happened on the screen
showing the sporting event.
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Applications requiring low-latency live media delivery are generally
feasible at scale with some restrictions. This typically requires
the use of a premium service dedicated to the delivery of live video,
and some tradeoffs may be necessary relative to what's feasible in a
higher latency service. The tradeoffs may include higher costs, or
delivering a lower quality video, or reduced flexibility for adaptive
bitrates, or reduced flexibility for available resolutions so that
fewer devices can receive an encoding tuned for their display. Low-
latency live delivery is also more susceptible to user-visible
disruptions due to transient network conditions than higher latency
services.
Implementation of a low-latency live video service can be achieved
with the use of low-latency extensions of HLS (called LL-HLS)
[I-D.draft-pantos-hls-rfc8216bis] and DASH (called LL-DASH)
[LL-DASH]. These extensions use the Common Media Application Format
(CMAF) standard [MPEG-CMAF] that allows the media to be packaged into
and transmitted in units smaller than segments, which are called
chunks in CMAF language. This way, the latency can be decoupled from
the duration of the media segments. Without a CMAF-like packaging,
lower latencies can only be achieved by using very short segment
durations. However, shorter segments means more frequent intra-coded
frames and that is detrimental to video encoding quality. CMAF
allows us to still use longer segments (improving encoding quality)
without penalizing latency.
While an LL-HLS client retrieves each chunk with a separate HTTP GET
request, an LL-DASH client uses the chunked transfer encoding feature
of the HTTP [CMAF-CTE] which allows the LL-DASH client to fetch all
the chunks belonging to a segment with a single GET request. An HTTP
server can transmit the CMAF chunks to the LL-DASH client as they
arrive from the encoder/packager. A detailed comparison of LL-HLS
and LL-DASH is given in [MMSP20].
3.3. Non-Low-Latency Live
Non-low-latency live delivery of media is defined here as a live
stream that does not have a latency target shorter than 10 seconds.
This level of latency is the historically common case for segmented
video delivery using HLS [RFC8216] and DASH [MPEG-DASH]. This level
of latency is often considered adequate for content like news or pre-
recorded content. This level of latency is also sometimes achieved
as a fallback state when some part of the delivery system or the
client-side players do not have the necessary support for the
features necessary to support low-latency live streaming.
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This level of latency can typically be achieved at scale with
commodity CDN services for HTTP(s) delivery, and in some cases the
increased time window can allow for production of a wider range of
encoding options relative to the requirements for a lower latency
service without the need for increasing the hardware footprint, which
can allow for wider device interoperability.
3.4. On-Demand
On-Demand media streaming refers to playback of pre-recorded media
based on a user's action. In some cases on-demand media is produced
as a by-product of a live media production, using the same segments
as the live event, but freezing the manifest after the live event has
finished. In other cases, on-demand media is constructed out of pre-
recorded assets with no streaming necessarily involved during the
production of the on-demand content.
On-demand media generally is not subject to latency concerns, but
other timing-related considerations can still be as important or even
more important to the user experience than the same considerations
with live events. These considerations include the startup time, the
stability of the media stream's playback quality, and avoidance of
stalls and video artifacts during the playback under all but the most
severe network conditions.
In some applications, optimizations are available to on-demand video
that are not always available to live events, such as pre-loading the
first segment for a startup time that doesn't have to wait for a
network download to begin.
4. Adaptive Encoding, Adaptive Delivery, and Measurement Collection
4.1. Overview
Adaptive BitRate (ABR) is a sort of application-level response
strategy in which the streaming client attempts to detect the
available bandwidth of the network path by observing the successful
application-layer download speed, then chooses a bitrate for each of
the video, audio, subtitles and metadata (among the limited number of
available options) that fits within that bandwidth, typically
adjusting as changes in available bandwidth occur in the network or
changes in capabilities occur during the playback (such as available
memory, CPU, display size, etc.).
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4.2. Adaptive Encoding
Media servers can provide media streams at various bitrates because
the media has been encoded at various bitrates. This is a so-called
"ladder" of bitrates, that can be offered to media players as part of
the manifest that describes the media being requested by the media
player, so that the media player can select among the available
bitrate choices.
The media server may also choose to alter which bitrates are made
available to players by adding or removing bitrate options from the
ladder delivered to the player in subsequent manifests built and sent
to the player. This way, both the player, through its selection of
bitrate to request from the manifest, and the server, through its
construction of the bitrates offered in the manifest, are able to
affect network utilization.
4.3. Adaptive Segmented Delivery
ABR playback is commonly implemented by streaming clients using HLS
[RFC8216] or DASH [MPEG-DASH] to perform a reliable segmented
delivery of media over HTTP. Different implementations use different
strategies [ABRSurvey], often relying on proprietary algorithms
(called rate adaptation or bitrate selection algorithms) to perform
available bandwidth estimation/prediction and the bitrate selection.
Many server-player systems will do an initial probe or a very simple
throughput speed test at the start of a video playback. This is done
to get a rough sense of the highest video bitrate in the ABR ladder
that the network between the server and player will likely be able to
provide under initial network conditions. After the initial testing,
clients tend to rely upon passive network observations and will make
use of player side statistics such as buffer fill rates to monitor
and respond to changing network conditions.
The choice of bitrate occurs within the context of optimizing for
some metric monitored by the client, such as highest achievable video
quality or lowest chances for a rebuffering event (playback stall).
This kind of bandwidth-measurement system can experience trouble in
several ways that can be affected by networking design choices.
Because adaptive application-level response strategies are typically
using application-level protocols, these mechanisms are affected by
transport-level protocol behaviors, and the application-level
feedback loop is interacting with a transport-level feedback loop, as
described in Section 4.3.1 and Section 4.3.2.
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4.3.1. Idle Time between Segments
When the bitrate selection is chosen substantially below the
available capacity of the network path, the response to a segment
request will typically complete in much less absolute time than the
duration of the requested segment, leaving significant idle time
between segment downloads. This can have a few surprising
consequences:
* TCP slow-start when restarting after idle requires multiple RTTs
to re-establish a throughput at the network's available capacity.
When the active transmission time for segments is substantially
shorter than the time between segments leaving an idle gap between
segments that triggers a restart of TCP slow-start, the estimate
of the successful download speed coming from the application-
visible receive rate on the socket can thus end up much lower than
the actual available network capacity, preventing a shift to the
most appropriate bitrate. [RFC7661] provides some mitigations for
this effect at the TCP transport layer, for senders who anticipate
a high incidence of this problem.
* Mobile flow-bandwidth spectrum and timing mapping can be impacted
by idle time in some networks. The carrier capacity assigned to a
link can vary with activity. Depending on the idle time
characteristics, this can result in a lower available bitrate than
would be achievable with a steadier transmission in the same
network.
Some receive-side ABR algorithms such as [ELASTIC] are designed to
try to avoid this effect. Another way to mitigate this effect is by
the help of two simultaneous TCP connections is explained in
[MMSys11] for Microsoft Smooth Streaming. In some cases, the system-
level TCP slow-start restart can be disabled [OReilly-HPBN].
4.3.2. Head-of-Line Blocking
In the event of a lost packet on a TCP connection with SACK support
(a common case for segmented delivery in practice), loss of a packet
can provide a confusing bandwidth signal to the receiving
application. Because of the sliding window in TCP, many packets may
be accepted by the receiver without being available to the
application until the missing packet arrives. Upon arrival of the
one missing packet after retransmit, the receiver will suddenly get
access to a lot of data at the same time.
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To a receiver measuring bytes received per unit time at the
application layer, and interpreting it as an estimate of the
available network bandwidth, this appears as a high jitter in the
goodput measurement.
It's worth noting that more modern transport protocols such as QUIC
have mitigation of head-of-line blocking as a protocol design goal.
See Section 5.3 for more details.
4.4. Measurement Collection
In addition to measurements media players use to guide their segment-
by-segment adaptive streaming requests, streaming media providers may
also rely on measurements collected from media players to provide
analytics that can be used for decisions such as whether the adaptive
encoding bitrates in use are the best ones to provide to media
players, or whether current media content caching is providing the
best experience for viewers.
In addition to measurements media players use to guide their segment-
by-segment adaptive streaming requests, streaming media providers may
also rely on measurements collected from media players to provide
analytics that can be used for decisions such as whether the adaptive
encoding bitrates in use are the best ones to provide to media
players, or whether current media content caching is providing the
best experience for viewers. To that effect, the Consumer Technology
Association (CTA) who owns the Web Application Video Ecosystem (WAVE)
project has published two important specifications.
4.4.1. CTA-2066: Streaming Quality of Experience Events, Properties and
Metrics
[CTA-2066] specifies a set of media player events, properties,
quality of experience (QoE) metrics and associated terminology for
representing streaming media quality of experience across systems,
media players and analytics vendors. While all these events,
properties, metrics and associated terminology is used across a
number of proprietary analytics and measurement solutions, they were
used in slightly (or vastly) different ways that led to
interoperability issues. CTA-2066 attempts to address this issue by
defining a common terminology as well as how each metric should be
computed for consistent reporting.
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4.4.2. CTA-5004: Common Media Client Data (CMCD)
Many assumes that the CDNs have a holistic view into the health and
performance of the streaming clients. However, this is not the case.
The CDNs produce millions of log lines per second across hundreds of
thousands of clients and they have no concept of a "session" as a
client would have, so CDNs are decoupled from the metrics the clients
generate and report. A CDN cannot tell which request belongs to
which playback session, the duration of any media object, the
bitrate, or whether any of the clients have stalled and are
rebuffering or are about to stall and will rebuffer. The consequence
of this decoupling is that a CDN cannot prioritize delivery for when
the client needs it most, prefetch content, or trigger alerts when
the network itself may be underperforming. One approach to couple
the CDN to the playback sessions is for the clients to communicate
standardized media-relevant information to the CDNs while they are
fetching data. [CTA-5004] was developed exactly for this purpose.
4.5. Unreliable Transport
In contrast to segmented delivery, several applications use
unreliable UDP or SCTP with its "partial reliability" extension
[RFC3758] to deliver Media encapsulated in RTP [RFC3550] or raw MPEG
Transport Stream ("MPEG-TS")-formatted video [MPEG-TS], when the
media is being delivered in situations such as broadcast and live
streaming, that better tolerate occasional packet loss without
retransmission.
Under congestion and loss, this approach generally experiences more
video artifacts with fewer delay or head-of-line blocking effects.
Often one of the key goals is to reduce latency, to better support
applications like videoconferencing, or for other live-action video
with interactive components, such as some sporting events.
The Secure Reliable Transport protocol [SRT] also uses UDP in an
effort to achieve lower latency for streaming media, although it adds
reliability at the application layer.
Congestion avoidance strategies for deployments using unreliable
transport protocols vary widely in practice, ranging from being
entirely unresponsive to congestion, to using feedback signaling to
change encoder settings (as in [RFC5762]), to using fewer enhancement
layers (as in [RFC6190]), to using proprietary methods to detect
"quality of experience" issues and turn off video in order to allow
less bandwidth-intensive media such as audio to be delivered.
More details about congestion avoidance strategies used with
unreliable transport protocols are included in Section 5.1.
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5. Evolution of Transport Protocols and Transport Protocol Behaviors
*Note to Reviewers*
This section includes some material on UDP and TCP that may be
tutorial for some readers. We can decide how to explain that, if the
working group feels that this tutorial material is worth keeping.
Spencer thought it was worth including, because it provides a
contrast to the material on QUIC, which is significantly less
tutorial, unless the reader participated in the QUIC working group.
Because networking resources are shared between users, a good place
to start our discussion is how contention between users, and
mechanisms to resolve that contention in ways that are "fair" between
users, impact streaming media users. These topics are closely tied
to transport protocol behaviors.
As noted in Section 4, Adaptive Bitrate response strategies such as
HLS [RFC8216] or DASH [MPEG-DASH] are attempting to respond to
changing path characteristics, and underlying transport protocols are
also attempting to respond to changing path characteristics.
For most of the history of the Internet, these transport protocols,
described in Section 5.1 and Section 5.2, have had relatively
consistent behaviors that have changed slowly, if at all, over time.
Newly standardized transport protocols like QUIC [RFC9000] can behave
differently from existing transport protocols, and these behaviors
may evolve over time more rapidly than currently-used transport
protocols.
For this reason, we have included a description of how the path
characteristics that streaming media providers may see are likely to
evolve over time.
5.1. UDP and Its Behavior
For most of the history of the Internet, we have trusted UDP-based
applications to limit their impact on other users. One of the
strategies used was to use UDP for simple query-response application
protocols, such as DNS, which is often used to send a single-packet
request to look up the IP address for a DNS name, and return a
single-packet response containing the IP address. Although it is
possible to saturate a path between a DNS client and DNS server with
DNS requests, in practice, that was rare enough that DNS included few
mechanisms to resolve contention between DNS users and other users
(whether they are also using DNS, or using other application
protocols).
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In recent times, the usage of UDP-based applications that were not
simple query-response protocols has grown substantially, and since
UDP does not provide any feedback mechanism to senders to help limit
impacts on other users, application-level protocols such as RTP
[RFC3550] have been responsible for the decisions that TCP-based
applications have delegated to TCP - what to send, how much to send,
and when to send it. So, the way some UDP-based applications
interact with other users has changed.
It's also worth pointing out that because UDP has no transport-layer
feedback mechanisms, UDP-based applications that send and receive
substantial amounts of information are expected to provide their own
feedback mechanisms. This expectation is most recently codified in
Best Current Practice [RFC8085].
RTP relies on RTCP Sender and Receiver Reports [RFC3550] as its own
feedback mechanism, and even includes Circuit Breakers for Unicast
RTP Sessions [RFC8083] for situations when normal RTP congestion
control has not been able to react sufficiently to RTP flows sending
at rates that result in sustained packet loss.
The notion of "Circuit Breakers" has also been applied to other UDP
applications in [RFC8084], such as tunneling packets over UDP that
are potentially not congestion-controlled (for example,
"Encapsulating MPLS in UDP", as described in [RFC7510]). If
streaming media is carried in tunnels encapsulated in UDP, these
media streams may encounter "tripped circuit breakers", with
resulting user-visible impacts.
5.2. TCP and Its Behavior
For most of the history of the Internet, we have trusted the TCP
protocol to limit the impact of applications that sent a significant
number of packets, in either or both directions, on other users.
Although early versions of TCP were not particularly good at limiting
this impact [RFC0793], the addition of Slow Start and Congestion
Avoidance, as described in [RFC2001], were critical in allowing TCP-
based applications to "use as much bandwidth as possible, but to
avoid using more bandwidth than was possible". Although dozens of
RFCs have been written refining TCP decisions about what to send, how
much to send, and when to send it, since 1988 [Jacobson-Karels] the
signals available for TCP senders remained unchanged - end-to-end
acknowledgments for packets that were successfully sent and received,
and packet timeouts for packets that were not.
The success of the largely TCP-based Internet is evidence that the
mechanisms TCP used to achieve equilibrium quickly, at a point where
TCP senders do not interfere with other TCP senders for sustained
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periods of time, have been largely successful. The Internet
continued to work even when the specific mechanisms used to reach
equilibrium changed over time. Because TCP provides a common tool to
avoid contention, as some TCP-based applications like FTP were
largely replaced by other TCP-based applications like HTTP, the
transport behavior remained consistent.
In recent times, the TCP goal of probing for available bandwidth, and
"backing off" when a network path is saturated, has been supplanted
by the goal of avoiding growing queues along network paths, which
prevent TCP senders from reacting quickly when a network path is
saturated. Congestion control mechanisms such as COPA [COPA18] and
BBR [I-D.cardwell-iccrg-bbr-congestion-control] make these decisions
based on measured path delays, assuming that if the measured path
delay is increasing, the sender is injecting packets onto the network
path faster than the receiver can accept them, so the sender should
adjust its sending rate accordingly.
Although TCP protocol behavior has changed over time, the common
practice of implementing TCP as part of an operating system kernel
has acted to limit how quickly TCP behavior can change. Even with
the widespread use of automated operating system update installation
on many end-user systems, streaming media providers could have a
reasonable expectation that they could understand TCP transport
protocol behaviors, and that those behaviors would remain relatively
stable in the short term.
5.3. The QUIC Protocol and Its Behavior
The QUIC protocol, developed from a proprietary protocol into an IETF
standards-track protocol [RFC9000], turns many of the statements made
in Section 5.1 and Section 5.2 on their heads.
Although QUIC provides an alternative to the TCP and UDP transport
protocols, QUIC is itself encapsulated in UDP. As noted elsewhere in
Section 6.1, the QUIC protocol encrypts almost all of its transport
parameters, and all of its payload, so any intermediaries that
network operators may be using to troubleshoot HTTP streaming media
performance issues, perform analytics, or even intercept exchanges in
current applications will not work for QUIC-based applications
without making changes to their networks. Section 6 describes the
implications of media encryption in more detail.
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While QUIC is designed as a general-purpose transport protocol, and
can carry different application-layer protocols, the current
standardized mapping is for HTTP/3 [I-D.ietf-quic-http], which
describes how QUIC transport features are used for HTTP. The
convention is for HTTP/3 to run over UDP port 443 [Port443] but this
is not a strict requirement.
When HTTP/3 is encapsulated in QUIC, which is then encapsulated in
UDP, streaming operators (and network operators) might see UDP
traffic patterns that are similar to HTTP(S) over TCP. Since earlier
versions of HTTP(S) rely on TCP, UDP ports may be blocked for any
port numbers that are not commonly used, such as UDP 53 for DNS.
Even when UDP ports are not blocked and HTTP/3 can flow, streaming
operators (and network operators) may severely rate-limit this
traffic because they do not expect to see legitimate high-bandwidth
traffic such as streaming media over the UDP ports that HTTP/3 is
using.
As noted in Section 4.3.2, because TCP provides a reliable, in-order
delivery service for applications, any packet loss for a TCP
connection causes "head-of-line blocking", so that no TCP segments
arriving after a packet is lost will be delivered to the receiving
application until the lost packet is retransmitted, allowing in-order
delivery to the application to continue. As described in [RFC9000],
QUIC connections can carry multiple streams, and when packet losses
do occur, only the streams carried in the lost packet are delayed.
A QUIC extension currently being specified ([I-D.ietf-quic-datagram])
adds the capability for "unreliable" delivery, similar to the service
provided by UDP, but these datagrams are still subject to the QUIC
connection's congestion controller, providing some transport-level
congestion avoidance measures, which UDP does not.
As noted in Section 5.2, there is increasing interest in transport
protocol behaviors that responds to delay measurements, instead of
responding to packet loss. These behaviors may deliver improved user
experience, but in some cases have not responded to sustained packet
loss, which exhausts available buffers along the end-to-end path that
may affect other users sharing that path. The QUIC protocol provides
a set of congestion control hooks that can be use for algorithm
agility, and [RFC9002] defines a basic algorithm with transport
behavior that is roughly similar to TCP NewReno [RFC6582]. However,
QUIC senders can and do unilaterally chose to use different
algorithms such as loss-based CUBIC [RFC8312], delay-based COPA or
BBR, or even something completely different
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We do have experience with deploying new congestion controllers
without melting the Internet (CUBIC is one example), but the point
mentioned in Section 5.2 about TCP being implemented in operating
system kernels is also different with QUIC. Although QUIC can be
implemented in operating system kernels, one of the design goals when
this work was chartered was "QUIC is expected to support rapid,
distributed development and testing of features", and to meet this
expectation, many implementers have chosen to implement QUIC in user
space, outside the operating system kernel, and to even distribute
QUIC libraries with their own applications.
The decision to deploy a new version of QUIC is relatively
uncontrolled, compared to other widely used transport protocols, and
this can include new transport behaviors that appear without much
notice except to the QUIC endpoints. At IETF 105, Christian Huitema
and Brian Trammell presented a talk on "Congestion Defense in Depth"
[CDiD], that explored potential concerns about new QUIC congestion
controllers being broadly deployed without the testing and
instrumentation that current major content providers routinely
include. The sense of the room at IETF 105 was that the current
major content providers understood what is at stake when they deploy
new congestion controllers, but this presentation, and the related
discussion in TSVAREA minutes from IETF 105 ([tsvarea-105], are still
worth a look for new and rapidly growing content providers.
It is worth considering that if TCP-based HTTP traffic and UDP-based
HTTP/3 traffic are allowed to enter operator networks on roughly
equal terms, questions of fairness and contention will be heavily
dependent on interactions between the congestion controllers in use
for TCP-base HTTP traffic and UDP-based HTTP/3 traffic.
More broadly, [I-D.ietf-quic-manageability] discusses manageability
of the QUIC transport protocol, focusing on the implications of
QUIC's design and wire image on network operations involving QUIC
traffic. It discusses what network operators can consider in some
detail.
6. Streaming Encrypted Media
"Encrypted Media" has at least three meanings:
* Media encrypted at the application layer, typically using some
sort of Digital Rights Management (DRM) system, and typically
remaining encrypted "at rest", when senders and receivers store
it,
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* Media encrypted by the sender at the transport layer, and
remaining encrypted until it reaches the ultimate media consumer
(in this document, referred to as "end-to-end media encryption"),
and
* Media encrypted by the sender at the transport layer, and
remaining encrypted until it reaches some intermediary that is
_not_ the ultimate media consumer, but has credentials allowing
decryption of the media content. This intermediary may examine
and even transform the media content in some way, before
forwarding re-encrypted media content (in this document referred
to as "hop-by-hop media encryption")
Both "hop-by-hop" and "end-to-end" encrypted transport may carry
media that is, in addition, encrypted at the application layer.
Each of these encryption strategies is intended to achieve a
different goal. For instance, application-level encryption may be
used for business purposes, such as avoiding piracy or enforcing
geographic restrictions on playback, while transport-layer encryption
may be used to prevent media steam manipulation or to protect
manifests.
This document does not take a position on whether those goals are
"valid" (whatever that might mean).
In this document, we will focus on media encrypted at the transport
layer, whether encrypted "hop-by-hop" or "end-to-end". Because media
encrypted at the application layer will only be processed by
application-level entities, this encryption does not have transport-
layer implications.
Both "End-to-End" and "Hop-by-Hop" media encryption have specific
implications for streaming operators. These are described in
Section 6.2 and Section 6.3.
6.1. General Considerations for Media Encryption
The use of strong encryption does provide confidentiality for
encrypted streaming media, from the sender to either an intermediary
or the ultimate media consumer, and this does prevent Deep Packet
Inspection by any intermediary that does not possess credentials
allowing decryption. However, even encrypted content streams may be
vulnerable to traffic analysis. An intermediary that can identify an
encrypted media stream without decrypting it, may be able to
"fingerprint" the encrypted media stream of known content, and then
match the targeted media stream against the fingerprints of known
content. This protection can be lessened if a media provider is
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repeatedly encrypting the same content. [CODASPY17] is an example of
what is possible when identifying HTTPS-protected videos over TCP
transport, based either on the length of entire resources being
transferred, or on characteristic packet patterns at the beginning of
a resource being transferred.
If traffic analysis is successful at identifying encrypted content
and associating it with specific users, this breaks privacy as
certainly as examining decrypted traffic.
Because HTTPS has historically layered HTTP on top of TLS, which is
in turn layered on top of TCP, intermediaries do have access to
unencrypted TCP-level transport information, such as retransmissions,
and some carriers exploited this information in attempts to improve
transport-layer performance [RFC3135]. The most recent standardized
version of HTTPS, HTTP/3 [I-D.ietf-quic-http], uses the QUIC protocol
[RFC9000] as its transport layer. QUIC relies on the TLS 1.3 initial
handshake [RFC8446] only for key exchange [RFC9001], and encrypts
almost all transport parameters itself, with the exception of a few
invariant header fields. In the QUIC short header, the only
transport-level parameter which is sent "in the clear" is the
Destination Connection ID [RFC8999], and even in the QUIC long
header, the only transport-level parameters sent "in the clear" are
the Version, Destination Connection ID, and Source Connection ID.
For these reasons, HTTP/3 is significantly more "opaque" than HTTPS
with HTTP/1 or HTTP/2.
6.2. Considerations for "Hop-by-Hop" Media Encryption
Although the IETF has put considerable emphasis on end-to-end
streaming media encryption, there are still important use cases that
require the insertion of intermediaries.
There are a variety of ways to involve intermediaries, and some are
much more intrusive than others.
From a content provider's perspective, a number of considerations are
in play. The first question is likely whether the content provider
intends that intermediaries are explicitly addressed from endpoints,
or whether the content provider is willing to allow intermediaries to
"intercept" streaming content transparently, with no awareness or
permission from either endpoint.
If a content provider does not actively work to avoid interception by
intermediaries, the effect will be indistinguishable from
"impersonation attacks", and endpoints cannot be assumed of any level
of privacy.
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Assuming that a content provider does intend to allow intermediaries
to participate in content streaming, and does intend to provide some
level of privacy for endpoints, there are a number of possible tools,
either already available or still being specified. These include
* Server And Network assisted DASH [MPEG-DASH-SAND] - this
specification introduces explicit messaging between DASH clients
and network elements or between various network elements for the
purpose of improving the efficiency of streaming sessions by
providing information about real-time operational characteristics
of networks, servers, proxies, caches, CDNs, as well as DASH
client's performance and status.
* "Double Encryption Procedures for the Secure Real-Time Transport
Protocol (SRTP)" [RFC8723] - this specification provides a
cryptographic transform for the Secure Real-time Transport
Protocol that provides both hop-by-hop and end-to-end security
guarantees.
* Secure Media Frames [SFRAME] - [RFC8723] is closely tied to SRTP,
and this close association impeded widespread deployment, because
it could not be used for the most common media content delivery
mechanisms. A more recent proposal, Secure Media Frames [SFRAME],
also provides both hop-by-hop and end-to-end security guarantees,
but can be used with other transport protocols beyond SRTP.
If a content provider chooses not to involve intermediaries, this
choice should be carefully considered. As an example, if media
manifests are encrypted end-to-end, network providers who had been
able to lower offered quality and reduce on their networks will no
longer be able to do that. Some resources that might inform this
consideration are in [RFC8825] (for WebRTC) and
[I-D.ietf-quic-manageability] (for HTTP/3 and QUIC).
6.3. Considerations for "End-to-End" Media Encryption
"End-to-end" media encryption offers the potential of providing
privacy for streaming media consumers, with the idea being that if an
unauthorized intermediary can't decrypt streaming media, the
intermediary can't use Deep Packet Inspection (DPI) to examine HTTP
request and response headers and identify the media content being
streamed.
"End-to-end" media encryption has become much more widespread in the
years since the IETF issued "Pervasive Monitoring Is an Attack"
[RFC7258] as a Best Current Practice, describing pervasive monitoring
as a much greater threat than previously appreciated. After the
Snowden disclosures, many content providers made the decision to use
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HTTPS protection - HTTP over TLS - for most or all content being
delivered as a routine practice, rather than in exceptional cases for
content that was considered "sensitive".
Unfortunately, as noted in [RFC7258], there is no way to prevent
pervasive monitoring by an "attacker", while allowing monitoring by a
more benign entity who "only" wants to use DPI to examine HTTP
requests and responses in order to provide a better user experience.
If a modern encrypted transport protocol is used for end-to-end media
encryption, intermediary streaming operators are unable to examine
transport and application protocol behavior. As described in
Section 6.2, only an intermediary streaming operator who is
explicitly authorized to examine packet payloads, rather than
intercepting packets and examining them without authorization, can
continue these practices.
[RFC7258] said that "The IETF will strive to produce specifications
that mitigate pervasive monitoring attacks", so streaming operators
should expect the IETF's direction toward preventing unauthorized
monitoring of IETF protocols to continue for the forseeable future.
7. IANA Considerations
This document requires no actions from IANA.
8. Security Considerations
This document introduces no new security issues.
9. Acknowledgments
Thanks to Mark Nottingham, Glenn Deen, Dave Oran, Aaron Falk, Kyle
Rose, Leslie Daigle, Lucas Pardue, Matt Stock, Alexandre Gouaillard,
and Mike English for their very helpful reviews and comments.
10. Informative References
[ABRSurvey]
Taani, B., Begen, A.C., Timmerer, C., Zimmermann, R., and
A. Bentaleb et al, "A Survey on Bitrate Adaptation Schemes
for Streaming Media Over HTTP", IEEE Communications
Surveys & Tutorials , 2019,
<https://ieeexplore.ieee.org/abstract/document/8424813>.
[CDiD] Huitema, C. and B. Trammell, "(A call for) Congestion
Defense in Depth", July 2019,
<https://datatracker.ietf.org/meeting/105/materials/
slides-105-tsvarea-congestion-defense-in-depth-00>.
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[CMAF-CTE] Law, W., "Ultra-Low-Latency Streaming Using Chunked-
Encoded and Chunked Transferred CMAF", October 2018,
<https://www.akamai.com/us/en/multimedia/documents/white-
paper/low-latency-streaming-cmaf-whitepaper.pdf>.
[CODASPY17]
Reed, A. and M. Kranch, "Identifying HTTPS-Protected
Netflix Videos in Real-Time", ACM CODASPY , March 2017,
<https://dl.acm.org/doi/10.1145/3029806.3029821>.
[COPA18] Arun, V. and H. Balakrishnan, "Copa: Practical Delay-Based
Congestion Control for the Internet", USENIX NSDI , April
2018, <https://web.mit.edu/copa/>.
[CTA-2066] Consumer Technology Association, "Streaming Quality of
Experience Events, Properties and Metrics", March 2020,
<https://shop.cta.tech/products/streaming-quality-of-
experience-events-properties-and-metrics>.
[CTA-5004] CTA, ., "Common Media Client Data (CMCD)", September 2020,
<https://shop.cta.tech/products/web-application-video-
ecosystem-common-media-client-data-cta-5004>.
[CVNI] "Cisco Visual Networking Index: Forecast and Trends,
2017-2022 White Paper", 27 February 2019,
<https://www.cisco.com/c/en/us/solutions/collateral/
service-provider/visual-networking-index-vni/white-paper-
c11-741490.html>.
[ELASTIC] De Cicco, L., Caldaralo, V., Palmisano, V., and S.
Mascolo, "ELASTIC: A client-side controller for dynamic
adaptive streaming over HTTP (DASH)", Packet Video
Workshop , December 2013,
<https://ieeexplore.ieee.org/document/6691442>.
[Encodings]
Apple, Inc, ., "HLS Authoring Specification for Apple
Devices", June 2020,
<https://developer.apple.com/documentation/
http_live_streaming/
hls_authoring_specification_for_apple_devices>.
[I-D.cardwell-iccrg-bbr-congestion-control]
Cardwell, N., Cheng, Y., Yeganeh, S. H., and V. Jacobson,
"BBR Congestion Control", Work in Progress, Internet-
Draft, draft-cardwell-iccrg-bbr-congestion-control-00, 3
July 2017, <https://www.ietf.org/archive/id/draft-
cardwell-iccrg-bbr-congestion-control-00.txt>.
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[I-D.draft-pantos-hls-rfc8216bis]
Pantos, R., "HTTP Live Streaming 2nd Edition", Work in
Progress, Internet-Draft, draft-pantos-hls-rfc8216bis-09,
27 April 2021, <https://www.ietf.org/archive/id/draft-
pantos-hls-rfc8216bis-09.txt>.
[I-D.ietf-quic-datagram]
Pauly, T., Kinnear, E., and D. Schinazi, "An Unreliable
Datagram Extension to QUIC", Work in Progress, Internet-
Draft, draft-ietf-quic-datagram-02, 16 February 2021,
<https://www.ietf.org/archive/id/draft-ietf-quic-datagram-
02.txt>.
[I-D.ietf-quic-http]
Bishop, M., "Hypertext Transfer Protocol Version 3
(HTTP/3)", Work in Progress, Internet-Draft, draft-ietf-
quic-http-34, 2 February 2021,
<https://www.ietf.org/archive/id/draft-ietf-quic-http-
34.txt>.
[I-D.ietf-quic-manageability]
Kuehlewind, M. and B. Trammell, "Manageability of the QUIC
Transport Protocol", Work in Progress, Internet-Draft,
draft-ietf-quic-manageability-11, 21 April 2021,
<https://www.ietf.org/archive/id/draft-ietf-quic-
manageability-11.txt>.
[IABcovid] Arkko, J., Farrel, S., Kuhlewind, M., and C. Perkins,
"Report from the IAB COVID-19 Network Impacts Workshop
2020", November 2020, <https://datatracker.ietf.org/doc/
draft-iab-covid19-workshop/>.
[Jacobson-Karels]
Jacobson, V. and M. Karels, "Congestion Avoidance and
Control", November 1988,
<https://ee.lbl.gov/papers/congavoid.pdf>.
[Labovitz] Labovitz, C., "Network traffic insights in the time of
COVID-19: April 9 update", April 2020,
<https://www.nokia.com/blog/network-traffic-insights-time-
covid-19-april-9-update/>.
[LabovitzDDoS]
Takahashi, D., "Why the game industry is still vulnerable
to DDoS attacks", May 2018,
<https://venturebeat.com/2018/05/13/why-the-game-industry-
is-still-vulnerable-to-distributed-denial-of-service-
attacks/>.
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[LL-DASH] DASH-IF, ., "Low-latency Modes for DASH", March 2020,
<https://dashif.org/docs/CR-Low-Latency-Live-r8.pdf>.
[Mishra] Mishra, S. and J. Thibeault, "An update on Streaming Video
Alliance", April 2020,
<https://datatracker.ietf.org/meeting/interim-2020-mops-
01/materials/slides-interim-2020-mops-01-sessa-april-
15-2020-mops-interim-an-update-on-streaming-video-
alliance>.
[MMSP20] Durak, K. and . et al, "Evaluating the performance of
Apple's low-latency HLS", IEEE MMSP , September 2020,
<https://ieeexplore.ieee.org/document/9287117>.
[MMSys11] Akhshabi, S., Begen, A.C., and C. Dovrolis, "An
experimental evaluation of rate-adaptation algorithms in
adaptive streaming over HTTP", ACM MMSys , February 2011,
<https://dl.acm.org/doi/10.1145/1943552.1943574>.
[MPEG-CMAF]
"ISO/IEC 23000-19:2020 Multimedia application format
(MPEG-A) - Part 19: Common media application format (CMAF)
for segmented media", March 2020,
<https://www.iso.org/standard/79106.html>.
[MPEG-DASH]
"ISO/IEC 23009-1:2019 Dynamic adaptive streaming over HTTP
(DASH) - Part 1: Media presentation description and
segment formats", December 2019,
<https://www.iso.org/standard/79329.html>.
[MPEG-DASH-SAND]
"ISO/IEC 23009-5:2017 Dynamic adaptive streaming over HTTP
(DASH) - Part 5: Server and network assisted DASH (SAND)",
February 2017, <https://www.iso.org/standard/69079.html>.
[MPEG-TS] "H.222.0 : Information technology - Generic coding of
moving pictures and associated audio information:
Systems", 29 August 2018,
<https://www.itu.int/rec/T-REC-H.222.0>.
[MPEGI] Boyce, J.M. and . et al, "MPEG Immersive Video Coding
Standard", Proceedings of the IEEE , n.d.,
<https://ieeexplore.ieee.org/document/9374648>.
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[OReilly-HPBN]
"High Performance Browser Networking (Chapter 2: Building
Blocks of TCP)", May 2021,
<https://hpbn.co/building-blocks-of-tcp/>.
[PCC] Schwarz, S. and . et al, "Emerging MPEG Standards for
Point Cloud Compression", IEEE Journal on Emerging and
Selected Topics in Circuits and Systems , March 2019,
<https://ieeexplore.ieee.org/document/8571288>.
[Port443] "Service Name and Transport Protocol Port Number
Registry", April 2021, <https://www.iana.org/assignments/
service-names-port-numbers/service-names-port-
numbers.txt>.
[RFC0793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, DOI 10.17487/RFC0793, September 1981,
<https://www.rfc-editor.org/info/rfc793>.
[RFC2001] Stevens, W., "TCP Slow Start, Congestion Avoidance, Fast
Retransmit, and Fast Recovery Algorithms", RFC 2001,
DOI 10.17487/RFC2001, January 1997,
<https://www.rfc-editor.org/info/rfc2001>.
[RFC3135] Border, J., Kojo, M., Griner, J., Montenegro, G., and Z.
Shelby, "Performance Enhancing Proxies Intended to
Mitigate Link-Related Degradations", RFC 3135,
DOI 10.17487/RFC3135, June 2001,
<https://www.rfc-editor.org/info/rfc3135>.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
July 2003, <https://www.rfc-editor.org/info/rfc3550>.
[RFC3758] Stewart, R., Ramalho, M., Xie, Q., Tuexen, M., and P.
Conrad, "Stream Control Transmission Protocol (SCTP)
Partial Reliability Extension", RFC 3758,
DOI 10.17487/RFC3758, May 2004,
<https://www.rfc-editor.org/info/rfc3758>.
[RFC4733] Schulzrinne, H. and T. Taylor, "RTP Payload for DTMF
Digits, Telephony Tones, and Telephony Signals", RFC 4733,
DOI 10.17487/RFC4733, December 2006,
<https://www.rfc-editor.org/info/rfc4733>.
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[RFC5594] Peterson, J. and A. Cooper, "Report from the IETF Workshop
on Peer-to-Peer (P2P) Infrastructure, May 28, 2008",
RFC 5594, DOI 10.17487/RFC5594, July 2009,
<https://www.rfc-editor.org/info/rfc5594>.
[RFC5762] Perkins, C., "RTP and the Datagram Congestion Control
Protocol (DCCP)", RFC 5762, DOI 10.17487/RFC5762, April
2010, <https://www.rfc-editor.org/info/rfc5762>.
[RFC6190] Wenger, S., Wang, Y.-K., Schierl, T., and A.
Eleftheriadis, "RTP Payload Format for Scalable Video
Coding", RFC 6190, DOI 10.17487/RFC6190, May 2011,
<https://www.rfc-editor.org/info/rfc6190>.
[RFC6582] Henderson, T., Floyd, S., Gurtov, A., and Y. Nishida, "The
NewReno Modification to TCP's Fast Recovery Algorithm",
RFC 6582, DOI 10.17487/RFC6582, April 2012,
<https://www.rfc-editor.org/info/rfc6582>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012,
<https://www.rfc-editor.org/info/rfc6817>.
[RFC6843] Clark, A., Gross, K., and Q. Wu, "RTP Control Protocol
(RTCP) Extended Report (XR) Block for Delay Metric
Reporting", RFC 6843, DOI 10.17487/RFC6843, January 2013,
<https://www.rfc-editor.org/info/rfc6843>.
[RFC7234] Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
Ed., "Hypertext Transfer Protocol (HTTP/1.1): Caching",
RFC 7234, DOI 10.17487/RFC7234, June 2014,
<https://www.rfc-editor.org/info/rfc7234>.
[RFC7258] Farrell, S. and H. Tschofenig, "Pervasive Monitoring Is an
Attack", BCP 188, RFC 7258, DOI 10.17487/RFC7258, May
2014, <https://www.rfc-editor.org/info/rfc7258>.
[RFC7510] Xu, X., Sheth, N., Yong, L., Callon, R., and D. Black,
"Encapsulating MPLS in UDP", RFC 7510,
DOI 10.17487/RFC7510, April 2015,
<https://www.rfc-editor.org/info/rfc7510>.
[RFC7656] Lennox, J., Gross, K., Nandakumar, S., Salgueiro, G., and
B. Burman, Ed., "A Taxonomy of Semantics and Mechanisms
for Real-Time Transport Protocol (RTP) Sources", RFC 7656,
DOI 10.17487/RFC7656, November 2015,
<https://www.rfc-editor.org/info/rfc7656>.
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[RFC7661] Fairhurst, G., Sathiaseelan, A., and R. Secchi, "Updating
TCP to Support Rate-Limited Traffic", RFC 7661,
DOI 10.17487/RFC7661, October 2015,
<https://www.rfc-editor.org/info/rfc7661>.
[RFC8083] Perkins, C. and V. Singh, "Multimedia Congestion Control:
Circuit Breakers for Unicast RTP Sessions", RFC 8083,
DOI 10.17487/RFC8083, March 2017,
<https://www.rfc-editor.org/info/rfc8083>.
[RFC8084] Fairhurst, G., "Network Transport Circuit Breakers",
BCP 208, RFC 8084, DOI 10.17487/RFC8084, March 2017,
<https://www.rfc-editor.org/info/rfc8084>.
[RFC8085] Eggert, L., Fairhurst, G., and G. Shepherd, "UDP Usage
Guidelines", BCP 145, RFC 8085, DOI 10.17487/RFC8085,
March 2017, <https://www.rfc-editor.org/info/rfc8085>.
[RFC8216] Pantos, R., Ed. and W. May, "HTTP Live Streaming",
RFC 8216, DOI 10.17487/RFC8216, August 2017,
<https://www.rfc-editor.org/info/rfc8216>.
[RFC8312] Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
RFC 8312, DOI 10.17487/RFC8312, February 2018,
<https://www.rfc-editor.org/info/rfc8312>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/info/rfc8446>.
[RFC8622] Bless, R., "A Lower-Effort Per-Hop Behavior (LE PHB) for
Differentiated Services", RFC 8622, DOI 10.17487/RFC8622,
June 2019, <https://www.rfc-editor.org/info/rfc8622>.
[RFC8723] Jennings, C., Jones, P., Barnes, R., and A.B. Roach,
"Double Encryption Procedures for the Secure Real-Time
Transport Protocol (SRTP)", RFC 8723,
DOI 10.17487/RFC8723, April 2020,
<https://www.rfc-editor.org/info/rfc8723>.
[RFC8825] Alvestrand, H., "Overview: Real-Time Protocols for
Browser-Based Applications", RFC 8825,
DOI 10.17487/RFC8825, January 2021,
<https://www.rfc-editor.org/info/rfc8825>.
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[RFC8999] Thomson, M., "Version-Independent Properties of QUIC",
RFC 8999, DOI 10.17487/RFC8999, May 2021,
<https://www.rfc-editor.org/info/rfc8999>.
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/info/rfc9000>.
[RFC9001] Thomson, M., Ed. and S. Turner, Ed., "Using TLS to Secure
QUIC", RFC 9001, DOI 10.17487/RFC9001, May 2021,
<https://www.rfc-editor.org/info/rfc9001>.
[RFC9002] Iyengar, J., Ed. and I. Swett, Ed., "QUIC Loss Detection
and Congestion Control", RFC 9002, DOI 10.17487/RFC9002,
May 2021, <https://www.rfc-editor.org/info/rfc9002>.
[SFRAME] "Secure Media Frames Working Group (Home Page)", n.d.,
<https://datatracker.ietf.org/doc/charter-ietf-sframe/>.
[SRT] Sharabayko, M., "Secure Reliable Transport (SRT) Protocol
Overview", 15 April 2020,
<https://datatracker.ietf.org/meeting/interim-2020-mops-
01/materials/slides-interim-2020-mops-01-sessa-april-
15-2020-mops-interim-an-update-on-streaming-video-
alliance>.
[tsvarea-105]
"TSVAREA Minutes - IETF 105", July 2019,
<https://datatracker.ietf.org/meeting/105/materials/
minutes-105-tsvarea-00>.
Authors' Addresses
Jake Holland
Akamai Technologies, Inc.
150 Broadway
Cambridge, MA 02144,
United States of America
Email: jakeholland.net@gmail.com
Ali Begen
Networked Media
Turkey
Email: ali.begen@networked.media
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Spencer Dawkins
Tencent America LLC
United States of America
Email: spencerdawkins.ietf@gmail.com
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