MOPS J. Holland
Internet-Draft Akamai Technologies, Inc.
Intended status: Informational A. Begen
Expires: 16 August 2020 Networked Media
S. Dawkins
Tencent America LLC
13 February 2020
Operational Considerations for Streaming Media
draft-ietf-mops-streaming-opcons-00
Abstract
This document provides an overview of operational networking issues
that pertain to quality of experience in delivery of video and other
high-bitrate media over the internet.
Status of This Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 16 August 2020.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Venues for Contribution and Discussion . . . . . . . . . 3
2. Bandwidth Provisioning . . . . . . . . . . . . . . . . . . . 3
2.1. Scaling Requirements for Media Delivery . . . . . . . . . 3
2.1.1. Video Bitrates . . . . . . . . . . . . . . . . . . . 3
2.1.2. Virtual Reality Bitrates . . . . . . . . . . . . . . 4
2.2. Path Requirements . . . . . . . . . . . . . . . . . . . . 4
2.3. Caching Systems . . . . . . . . . . . . . . . . . . . . . 5
2.4. Predictable Usage Profiles . . . . . . . . . . . . . . . 5
2.5. Unpredictable Usage Profiles . . . . . . . . . . . . . . 5
3. Adaptive Bitrate . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2. Segmented Delivery . . . . . . . . . . . . . . . . . . . 7
3.2.1. Idle Time between Segments . . . . . . . . . . . . . 7
3.2.2. Head-of-Line Blocking . . . . . . . . . . . . . . . . 7
3.3. Unreliable Transport . . . . . . . . . . . . . . . . . . 8
4. Doc History and Side Notes . . . . . . . . . . . . . . . . . 8
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
6. Security Considerations . . . . . . . . . . . . . . . . . . . 9
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
8. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
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
[CVNI]).
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.
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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. Venues for Contribution and Discussion
Note to RFC Editor: Please remove this section before publication
(To the editor: check this repository URL after the draft is adopted.
The working group may create its own repository)
This document is in the Github repository at
https://github.com/GrumpyOldTroll/ietf-mops-drafts. 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).
2. Bandwidth Provisioning
2.1. Scaling Requirements for Media Delivery
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]:
Kbps = (HEIGHT * WIDTH * FRAME_RATE) / (15 * 1024)
Height and width are in pixels, and frame rate is in frames per
second. The actual bitrate required for a specific video will also
depend on the codec used, fidelity desired and some other
characteristics of the video itself, such as the amount and frequency
of high-detail motion, which may influence the compressability of the
content, but this equation provides a rough estimate.
Here are a few common resolutions used for video content, with their
typical per-user bandwidth requirements according to this formula:
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+------------+----------------+-------------------------------+
| Name | Width x Height | Approximate Bitrate for 60fps |
+============+================+===============================+
| DVD | 720 x 480 | 1.3 Mbps |
+------------+----------------+-------------------------------+
| 720p (1K) | 1280 x 720 | 3.6 Mbps |
+------------+----------------+-------------------------------+
| 1080p (2K) | 1920 x 1080 | 8.1 Mbps |
+------------+----------------+-------------------------------+
| 2160p (4k) | 3840 x 2160 | 32 Mbps |
+------------+----------------+-------------------------------+
Table 1
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 [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 up to 80 Gbps would be necessary in order for all of
them to get 1080p resolution at 60 fps.
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
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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
TBD: pros, cons, tradeoffs of caching designs at different locations
within the network?
Peak vs. average provisioning, and effects on peering point
congestion under peak load?
Provisioning issues for caching systems?
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?
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
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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
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.
3. Adaptive Bitrate
3.1. Overview
Adaptive BitRate (ABR) is a sort of application-level response
strategy in which the receiving media player attempts to detect the
available bandwidth of the network path by experiment or by observing
the successful application-layer download speed, then chooses a video
bitrate (among the limited number of available options) that fits
within that bandwidth, typically adjusting as changes in available
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bandwidth occur in the network or changes in capabilities occur in
the player (such as available memory, CPU, display size, etc.).
The choice of bitrate occurs within the context of optimizing for
some metric monitored by the video player, such as highest achievable
video quality, or lowest rate of expected rebuffering events.
3.2. Segmented Delivery
ABR playback is commonly implemented by video players using HLS
[RFC8216] or DASH [DASH] to perform a reliable segmented delivery of
video data over HTTP. Different player implementations and receiving
devices use different strategies, often proprietary algorithms
(called rate adaptation or bitrate selection algorithms), to perform
available bandwidth estimation/prediction and the bitrate selection.
Most players 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
[NOSSDAV12].
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
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one missing packet after retransmit, the receiver will suddenly get
access to a lot of data at the same time.
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. Doc History and Side Notes
Note to RFC Editor: Please remove this section before publication
TBD: suggestion from mic at IETF 106 (Mark Nottingham): dive into the
different constraints coming from different parts of the network or
distribution channels. (regarding questions about how to describe the
disconnect between demand vs. capacity, while keeping good archival
value.) https://www.youtube.com/watch?v=4_k340xT2jM&t=13m
TBD: suggestion from mic at IETF 106 (Dave Oran + Glenn Deen
responding): pre-placement for many use cases is useful-distinguish
between live vs. cacheable. "People assume high-demand == live, but
not always true" with popular netflix example.
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(Glenn): something about latency requirements for cached vs.
streaming on live vs. pre-recorded content, and breaking
requirements into 2 separate charts. also: "Standardized ladder" for
adaptive bit rate rates suggested, declined as out of scope.
https://www.youtube.com/watch?v=4_k340xT2jM&t=14m15s
TBD: suggestion at the mic from IETF 106 (Aaron Falk): include
industry standard metrics from citations, some standard scoping
metrics may be already defined. https://www.youtube.com/
watch?v=4_k340xT2jM&t=19m15s
5. IANA Considerations
This document requires no actions from IANA.
6. Security Considerations
This document introduces no new security issues.
7. Acknowledgements
(Your name could go here!)
8. Informative References
[CVNI] Cisco Systems, Inc., "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>.
[DASH] "Information technology -- Dynamic adaptive streaming over
HTTP (DASH) -- Part 1: Media presentation description and
segment formats", ISO/IEC 23009-1:2019, 2019.
[MSOD] Akamai Technologies, Inc., "Media Services On Demand:
Encoder Best Practices", 2019, <https://learn.akamai.com/
en-us/webhelp/media-services-on-demand/media-services-on-
demand-encoder-best-practices/GUID-7448548A-A96F-4D03-
9E2D-4A4BBB6EC071.html>.
[NOSSDAV12]
al., S.A.e., "What Happens When HTTP Adaptive Streaming
Players Compete for Bandwidth?", June 2012,
<https://dl.acm.org/doi/10.1145/2229087.2229092>.
[PCC] al., S.S.e., "Emerging MPEG Standards for Point Cloud
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Compression", March 2019,
<https://ieeexplore.ieee.org/document/8571288>.
[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,
<https://www.rfc-editor.org/info/rfc2309>.
[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,
<https://www.rfc-editor.org/info/rfc3168>.
[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>.
[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,
<https://www.rfc-editor.org/info/rfc8033>.
[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>.
Authors' Addresses
Jake Holland
Akamai Technologies, Inc.
150 Broadway
Cambridge, MA 02144,
United States of America
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Email: jakeholland.net@gmail.com
Ali Begen
Networked Media
Turkey
Email: ali.begen@networked.media
Spencer Dawkins
Tencent America LLC
United States of America
Email: spencerdawkins.ietf@gmail.com
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