MOPS                                                          J. Holland
Internet-Draft                                 Akamai Technologies, Inc.
Intended status: Informational                                  A. Begen
Expires: 12 November 2021                                Networked Media
                                                              S. Dawkins
                                                     Tencent America LLC
                                                             11 May 2021

             Operational Considerations for Streaming Media


   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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   Drafts is at

   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 12 November 2021.

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   Copyright (c) 2021 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Simplified BSD License.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Notes for Contributors and Reviewers  . . . . . . . . . .   3
       1.1.1.  Venues for Contribution and Discussion  . . . . . . .   4
       1.1.2.  Template for Contributions  . . . . . . . . . . . . .   4
       1.1.3.  History of Public Discussion  . . . . . . . . . . . .   5
   2.  Bandwidth Provisioning  . . . . . . . . . . . . . . . . . . .   5
     2.1.  Scaling Requirements for Media Delivery . . . . . . . . .   5
       2.1.1.  Video Bitrates  . . . . . . . . . . . . . . . . . . .   6
       2.1.2.  Virtual Reality Bitrates  . . . . . . . . . . . . . .   7
     2.2.  Path Requirements . . . . . . . . . . . . . . . . . . . .   7
     2.3.  Caching Systems . . . . . . . . . . . . . . . . . . . . .   8
     2.4.  Predictable Usage Profiles  . . . . . . . . . . . . . . .   8
     2.5.  Unpredictable Usage Profiles  . . . . . . . . . . . . . .   9
     2.6.  Extremely Unpredictable Usage Profiles  . . . . . . . . .  10
   3.  Adaptive Bitrate  . . . . . . . . . . . . . . . . . . . . . .  11
     3.1.  Overview  . . . . . . . . . . . . . . . . . . . . . . . .  11
     3.2.  Segmented Delivery  . . . . . . . . . . . . . . . . . . .  12
       3.2.1.  Idle Time between Segments  . . . . . . . . . . . . .  12
       3.2.2.  Head-of-Line Blocking . . . . . . . . . . . . . . . .  12
     3.3.  Unreliable Transport  . . . . . . . . . . . . . . . . . .  13
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  13
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

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.  What's more, 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

   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

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   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, continous 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, server's transmission rate must
   (loosely or tightly) match to client's consumption rate for an
   uninterrupted playback.  That is, the client must not run out of data
   (buffer underrun) or take more than it can keep (buffer overrun) as
   any excess media is simply discarded.  Second, client's consumption
   rate is limited by not only bandwidth availability but also the real-
   time constraints.  That is, the client cannot fetch media that is not
   available yet.

   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.

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1.1.1.  Venues for Contribution and Discussion

   This document is in the Github repository at:

   Readers are welcome to open issues and send pull requests for this

   Substantial discussion of this document should take place on the MOPS
   working group mailing list (

   *  Join:

   *  Search:

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:

   Contributions can be emailed to, submitted as issues to
   the issue tracker of the repository in Section 1.1.1, or emailed to
   the document authors at

   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

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      -  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.

      -  supplemental information about the technique's deployment
         prevalence and status

1.1.3.  History of Public Discussion


   *  IETF 105 BOF:

   *  IETF 106 meeting:

   *  MOPS Interim Meeting 2020-04-15:

   *  IETF 108 meeting:

   *  MOPS 2020-10-30 Interim meeting:

2.  Bandwidth Provisioning

2.1.  Scaling Requirements for Media Delivery

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2.1.1.  Video Bitrates

   Video bitrate selection depends on many variables.  Different
   providers give different guidelines, but an equation that
   approximately matches the bandwidth requirement estimates from
   several video providers is given in [MSOD]:


   Height and width are in pixels, frame rate is in frames per second,
   and the motion factor is a value that ranges from 20 for a low-motion
   talking heads video to 7 for sports, and content with a lot of screen

   The motion factor captures the variability in bitrate due to the
   amount and frequency of high-detail motion, which generally
   influences the compressability of the content.

   The exact bitrate required for a particular video also depends on a
   number of specifics about the codec used and how the codec-specific
   tuning parameters are matched to the content, but this equation
   provides a rough estimate that approximates the usual bitrate
   characteristics using the most common codecs and settings for
   production traffic.

   Here are a few common resolutions used for video content, with their
   typical and peak per-user bandwidth requirements for 60 frames per
   second (FPS):

           | Name       | Width x Height | Typical  | Peak    |
           | DVD        | 720 x 480      | 1.3 Mbps | 3 Mbps  |
           | 720p (1K)  | 1280 x 720     | 3.6 Mbps | 5 Mbps  |
           | 1080p (2K) | 1920 x 1080    | 8.1 Mbps | 18 Mbps |
           | 2160p (4k) | 3840 x 2160    | 32 Mbps  | 70 Mbps |

                                 Table 1

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2.1.2.  Virtual Reality Bitrates

   Even the basic virtual reality (360-degree) videos (that allow users
   to look around freely, referred to as three degrees of freedom -
   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 basic user movement (3DoF+) or
   full user movement (6DoF) is allowed, the required bitrate grows even
   further.  In this case, the 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.

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 would be necessary in order for all of them to
   get typical content at 1080p resolution at 60 fps, or up to 180 Gbps
   to get sustained high-motion content such as sports, while
   maintaining the same 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.

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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 also "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.

   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, and could be pre-loaded
   during off-peak hours, using "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 entertainement as
   well as conversational video.

   TBD: insert charts showing historical relative data usage patterns
   with error bars by time of day in consumer networks?

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   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 quite large.

   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 asymetric,
   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 does not, because application bandwidth usage
   patterns have changed.

   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

   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.

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   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,

   *  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

   *  A 30% increase in upstream traffic over their pre-pandemic levels,

   *  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 Inernet 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.

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   *  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
      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.  Adaptive Bitrate

3.1.  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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   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 (playback stall).

3.2.  Segmented Delivery

   ABR playback is commonly implemented by streaming clients using HLS
   [RFC8216] or DASH [DASH] to perform a reliable segmented delivery of
   media over HTTP.  Different implementations use different strategies
   [ABRSurvey], often proprietary algorithms (called rate adaptation or
   bitrate selection algorithms) to perform available bandwidth
   estimation/prediction and the bitrate selection.  Most clients only
   use passive observations, i.e., they do not generate probe traffic to
   measure the available bandwidth.

   This kind of bandwidth-measurement systems can experience trouble in
   several ways that can be affected by networking design choices.

3.2.1.  Idle Time between Segments

   When the bitrate selection is successfully chosen below the available
   capacity of the network path, the response to a segment request will
   typically complete in less absolute time than the duration of the
   requested segment.  The resulting idle time within the connection
   carrying the segments has a few surprising consequences:

   *  Mobile flow-bandwidth spectrum and timing mapping.

   *  TCP slow-start when restarting after idle requires multiple RTTs
      to re-establish a throughput at the network's available capacity.
      On high-RTT paths or with small enough segments, this can produce
      a falsely low application-visible measurement of the available
      network capacity.

   A detailed investigation of this phenomenon is available in

3.2.2.  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.

   Active Queue Management (AQM) systems such as PIE [RFC8033] or
   variants of RED [RFC2309] that induce early random loss under
   congestion can mitigate this by using ECN [RFC3168] where available.
   ECN provides a congestion signal and induce a similar backoff in
   flows that use Explicit Congestion Notification-capable transport,
   but by avoiding loss avoids inducing head-of-line blocking effects in
   TCP connections.

3.3.  Unreliable Transport

   In contrast to segmented delivery, several applications use UDP or
   unreliable SCTP to deliver RTP or raw TS-formatted video.

   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.

   Congestion avoidance strategies for this kind of deployment vary
   widely in practice, ranging from some streams that are entirely
   unresponsive to using feedback signaling to change encoder settings
   (as in [RFC5762]), or to use fewer enhancement layers (as in
   [RFC6190]), to proprietary methods for detecting quality of
   experience issues and cutting off video.

4.  IANA Considerations

   This document requires no actions from IANA.

5.  Security Considerations

   This document introduces no new security issues.

6.  Acknowledgements

   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.

7.  Informative References

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              Abdelhak Bentaleb et al, ., "A Survey on Bitrate
              Adaptation Schemes for Streaming Media Over HTTP", 2019,

   [CVNI]     Cisco Systems, Inc, ., "Cisco Visual Networking Index:
              Forecast and Trends, 2017-2022 White Paper", 27 February
              2019, <

   [DASH]     "Information technology -- Dynamic adaptive streaming over
              HTTP (DASH) -- Part 1: Media presentation description and
              segment formats", ISO/IEC 23009-1:2019, 2019,

   [IABcovid] Jari Arkko / Stephen Farrel / Mirja K├╝hlewind / Colin
              Perkins, ., "Report from the IAB COVID-19 Network Impacts
              Workshop 2020", November 2020,

   [Labovitz] Labovitz, C. and Nokia Deepfield, "Network traffic
              insights in the time of COVID-19: April 9 update", April
              2020, <

              Takahashi, D. and Venture Beat, "Why the game industry is
              still vulnerable to DDoS attacks", May 2018,

   [Mishra]   Mishra, S. and J. Thibeault, "An update on Streaming Video
              Alliance", 2020, <

   [MPEGI]    Boyce et al, J.M., "MPEG Immersive Video Coding Standard",
              n.d., <>.

   [MSOD]     Akamai Technologies, Inc, ., "Media Services On Demand:
              Encoder Best Practices", 2019, <

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              Saamer Akhshabi et al, ., "What Happens When HTTP Adaptive
              Streaming Players Compete for Bandwidth?", June 2012,

   [PCC]      Sebastian Schwarz et al, ., "Emerging MPEG Standards for
              Point Cloud Compression", March 2019,

   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998,

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,

   [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,

   [RFC5762]  Perkins, C., "RTP and the Datagram Congestion Control
              Protocol (DCCP)", RFC 5762, DOI 10.17487/RFC5762, April
              2010, <>.

   [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,

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,

   [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,

Holland, et al.         Expires 12 November 2021               [Page 15]

Internet-Draft             Media Streaming Ops                  May 2021

   [RFC8033]  Pan, R., Natarajan, P., Baker, F., and G. White,
              "Proportional Integral Controller Enhanced (PIE): A
              Lightweight Control Scheme to Address the Bufferbloat
              Problem", RFC 8033, DOI 10.17487/RFC8033, February 2017,

   [RFC8216]  Pantos, R., Ed. and W. May, "HTTP Live Streaming",
              RFC 8216, DOI 10.17487/RFC8216, August 2017,

   [RFC8622]  Bless, R., "A Lower-Effort Per-Hop Behavior (LE PHB) for
              Differentiated Services", RFC 8622, DOI 10.17487/RFC8622,
              June 2019, <>.

Authors' Addresses

   Jake Holland
   Akamai Technologies, Inc.
   150 Broadway
   Cambridge, MA 02144,
   United States of America


   Ali Begen
   Networked Media


   Spencer Dawkins
   Tencent America LLC
   United States of America


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