“Supporting Wireless Video Growth and Trends”

4G Americas Whitepaper April 2013

IEEE CVT meeting, September 2013

Kamakshi Sridhar, Ph.D

Director, Wireless CTO

Alcatel-Lucent

Whitepaper jointly led with

Gautam Talagery

Director, Mobile Broadband, Ericsson

Contributors and Reviewers

Whitepaper benefited from

active participation and

contributions from operators

(TEF, Rogers, Lime), vendors

(ALU, Ericsson), device

manufacturer (QCOM) leading

to very good discussions

addressing different

perspectives.

Javier Lorca - Telefonica

Tony Lutz – Qualcomm

Dave Robinson – Alcatel-Lucent

Harish Viswanathan – Alcatel-

Lucent

Barry Pratt – Rogers

Cleverston Miller – Lime

Ram Venkatramani – previously

from Openwave Mobility

Karri Kuoppamaki – T-Mobile

Gautam Talagery – Ericsson

Kamakshi Sridhar - ALU

4G Americas Whitepaper

“Supporting Wireless Video Growth and Trends”

4G Americas Whitepaper April 2013

• http://www.4gamericas.org/documents/4G%20Americas%20-

Supporting%20Mobile%20Video%20Growth%20and%20Trends%20April%20

2013.pdf

www.PresentationPro.com

Goals

• Drivers and trends

• Types of video

– Short/long form,

– HTTP-Adaptive Streaming and HTTP – Progressive Download

RF and core challenges and solutions:

• Spectral efficiency, Varying channel, Mobility

• Signaling and battery issues

• Backhaul limitations

• HAS, TCP interactions, and role of client optimizations

• Video QoE metrics

• Impact of Codecs

• Architectures: HET-NET, CDN and eMBMS

• Role of Video playout buffers

• LTE QCI, and GBR

Fact: Mobile video is growing driven by smartphones and tablets, and new apps. What enables the successful, mass adoption of mobile video?

White paper examines

issues specific to video

that affect its delivery

over wireless networks.

We provide recommendations to app developers, and on bit rates for various codecs.

Market trends in Video Delivery/Market analysis

Streaming traffic during live events is high – (2009 statistics)

You-Tube and Real Time Entertainment traffic is growing Video/Audio streaming is growing

Key trends from Sandvine report, Ooyala, BBC statistics summarized to illustrate growth of mobile video.

Types of video

Video content can be classified as:

• Short form content, Long form content (based on duration)

• Operator video and OTT video (delivered by the service provider or not)

• Best Effort video and Guaranteed Bit Rate video (based on quality)

• Video for real time communication and for entertainment (based on usage)

Major video delivery technologies:

• Non-HTTP Real time streaming protocols (RTP/RTSP delivered video). To stream live video content to proprietary clients.

• HTTP Progressive Download (HTTP-PD): Access before download

• HTTP Adaptive Streaming (HAS): Video divided into chunks.

MPEG DASH (Dynamic Adaptive Streaming over HTTP) is a standard for

adaptive streaming over HTTP that replaces existing proprietary technologies

A unified standard is helpful to content publishers, who could produce one

set of files that play on all DASH-compatible devices.

Key requirements of mobile video delivery

Video flows are typically long duration flows.

Video QoE must be sustained over long durations, over RF, core.

For good video QoE, RF, backhaul and core challenges must be addressed e2e.

• Video must adapt to changes in link bandwidth.

• Video quality during handoffs must be managed

• Video quality as the cell becomes increasingly congested must be maintained.

• Must maintain the video quality (data rate, jitter specifications), fairness

– Video places demanding requirements on bit rate, jitter, and latency

• Must differentiate video quality according to subscription level

• Need to provide scalable solutions with large number of video flows

…. and competing traffic flows

• Need to consider impact of video and audio codec performance

• Must identify proper metrics to assess video QoE.

• Need to address impact of user device screen size, client on video QoE.

Impact of mobility, backhaul and

wireless core on video delivery

Data and throughput consumed by streaming

applications is High, particularly for Tablets

Paper examines: RF impact on mobile video.

– Spectral efficiency of 3G/4G cellular

technologies

– Mobility, Handovers, retransmissions, varying channel quality

Paper addresses

• Air interface latency:

– Challenge for interactive wireless - high-

quality videoconferencing or online gaming

– Progressive download video not so

sensitive

• Signaling challenges for Video - limited

• Adaptive streaming - best for battery

consumption – and network load

• Backhaul congestion impact, and role of traffic

shaping on video QoE.

• Encryption, Content rights management

Application Throughput

(Mbps)

MByte/Hour Hrs./day GB/month

0.5 0.9

1 1.7

2 3.5

4 6.9

0.5 1.4

1 2.7

2 5.4

4 10.8

0.5 3.4

1 6.8

2 13.5

4 27

0.5 6.8

1 13.5

2 27

4 54

0.5 13.5

1 27

2 54

4 108

Stereo Music 0.1 58

Small Screen Video (e.g. Feature

Phone)

0.2 90

Medium Screen Video (e.g.

Smartphone)

0.5 225

Medium Screen Video (e.g. Tablet) 1 450

Larger Screen Video (e.g. High

Quality on Tablet)

2 900

Video codecs and HTTP Adaptive Streaming (HAS)

• Popular video codecs include H.264, VP6, VP8, etc.

Codec (compression/decompression) algorithms play a key role.

• New/Upcoming codecs (HEVC): HEVC is a successor to the current state of

the art video coder - H.264/AVC video coding standard.

– With large consumption rates, HEVC would result in significant reduction

in infrastructure costs due to lower bandwidth required.

• HAS works by breaking the video into chunks, and each chunk is encoded

and sent out at an appropriate bit rate based on the channel quality as

estimated by the client. Without buffering at the client end (like in PD), HAS

is able to provide adequate QoE at the user end, without freezing video frame

• All HAS solutions have the same underlying architecture - server side

contains segmented video sequences and manifest file that describes content

properties and lists segment URLs. Segments are offered at different bit rate

levels which allow switching between bitrates when needed.

HTTP Adaptive Streaming (HAS)

• The paper also describes the BEHAVIOR OF VIDEO HAS CHUNKS WITH

TCP/IP particularly with poorly designed HAS clients.

• Issues such as Unfairness and Instability: Under Utilised TCP throughput

due to the interactions between HAS rate determining algorithm and TCP

congestion control resulting in downward spiral are described.

• We note that the important role of well designed clients and how they can

overcome these issues with HAS/TCP.

• Through subjective testing, it has been shown that HAS delivers an

acceptable video quality over a range of typical network conditions.

• A well-designed HAS client should be able to work well and share fairly with

other HTTP flows without knowing about the other devices that share the

network. The network fairly provides as much data downloading capacity as it

can to each end device. The HAS client on the device then decides which bit

rates to download at each point in time to provide the best quality experience

to the end user (i.e., no stalls or skips and a playback quality that pretty much

matches the current download rate of the HAS client).

Video Quality of Experience:

metrics and monitoring

Typical video QoE problems: Blurring, blocking, stall, skips, encoding dependencies

Must consider the impact of:

•Device size, viewing distance

•Playout issues, and varying image quality due to adaptive streaming

The algorithm used in the buffering or playout of video from a source can influence:

•Network bandwidth use; battery usage; QoE with buffered or delayed video

Metrics for characterizing video QoE – MOS: average and standard deviation

•Three classes of monitoring Video QoE as: Full/Reduced/Non Reference

•Main factors that influence end user QoE of streamed video are identified, including device and media factors and network factors.

•3GPP 26.247 parameters for HAS content.

•Work by the NGMN P-SERQU maps video chunk quality to a predicted MOS

Recommendation:

•Monitoring of Video QoE through monitoring the quality of HTTP chunks rather than individual radio and TCP parameters is preferred option.

Architectures for mobile video delivery

• Cellular (HET-NET) and WiFi offload architectures:

Macro/pico/femtos, role of WiFi

• LTE broadcast architecture –eMBMS

eMBMS can support live streaming of sports, high attach rate content such as

breaking news, and background file delivery.

• Content Delivery Networks

Distributed caches containing copies of data, to avoid server bottlenecks

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The main method of video optimization at the base

station is by controlling the data rate delivered to the

device.

LTE provides powerful QoS mechanisms.

End-to-end QoS support in LTE for video delivery

must consider:

• Role of LTE QCI and LTE guaranteed bit rate

bearers

• Parameter settings for HAS: GBR, MBR,

• Differentiation and fairness with GBR service

• Limitations of GBR bearer

• Adapting Video usage behavior through yield

management techniques.

LTE QCIs: 3GPP TS 23.203

Optimization and Mitigation techniques

QCI Resource Type

Priority Packet Delay

Budget (NOTE 1)

Packet Error Loss Rate

(NOTE 2)

Example Services

1

2 100 ms 10-2

Conversational Voice

2

GBR

4 150 ms 10-3

Conversational Video (Live Streaming)

3

3 50 ms 10-3

Real Time Gaming

4

5 300 ms 10-6

Non-Conversational Video (Buffered Streaming)

5

1 100 ms 10-6

IMS Signalling

6

6

300 ms

10-6

Video (Buffered Streaming) TCP-based (e.g., www, e-mal, chat, ftp, p2p file sharing, progressive video, etc.)

7

Non-GBR 7

100 ms

10-3

Voice, Video (Live Streaming) Interactive Gaming

8

8

300 ms

10-6

Video (Buffered Streaming) TCP-based (e.g., www, e-mail, chat, ftp, p2p file

9

9 sharing, progressive video, etc.)

Non-RF specific optimizations for video delivery

• Transcoding – Adapting encoding of video stream to available bandwidth at the device.

• Transrating - Changing resolution, fidelity, frame rate without full decode / encode

• Rate capping – An intermediate node adjusts the sending rate of a progressively downloaded video

• Scalable Video Codecs (SVC): The content encoded using a base layer and one or more enhancement layers

• Content caching in the network … useful but does not solve RAN congestion.

• Time shifting or side loading (over WiFi) supported by content caching in the UE – downloading, caching video content to the device during off-peak times or when the device is connected to WiFi.

• Congestion Triggered Optimization (for HTTP-PD) - Video Optimization engine dynamically starts optimizing (transrating/transcoding) video traffic during periods of congestion.

• Microbursting - High video data rate, within a short time to fill up the client buffers

Video delivery with CDNs

Content capture and ingestion.

• Video is compressed and transmitted to content ingestion sites

• Coding the video into multiple versions for different codec technologies, container formats, resolutions, and content variances (e.g. trailers or short clips, embedded captions) etc.

• Application of Digital Rights Management technology adopted.

• Access policies, restrictions control access to the published contents and advertisement placements.

Content distribution to local video headends follows the content ingestion:

• Video created sent over high speed networks to different video headends.

• Video delivery begins in video head end with supporting caching CDN nodes.

• Transcoding content into format appropriate for a particular device.

• The CDN server processes the request from end device, extracts device information from a subscription data base and delivers the video, transcoding if necessary.

• The delivery server maintains the session with the end device streaming the video as the end device consumes the content.

• The video server may additional invoke the APIs provided by the mobile core to establish QoS for the session.

Recommendations on bit rates for various codecs and screen sizes/device types

• The choice of bit rate for the audio and video streams is a compromise between

quality and bandwidth used for streaming. The device screen size and viewing

distance are a critical aspect in determining the bandwidth needed for encoding the

video.

– Scenes with high motion or scenes with a lot of detail require more bandwidth.

– Chaotic motion is the hardest to encode and requires higher bandwidth

• Compression efficiency of the video codec impacts the bandwidth needed to encode a

video quality with a given fidelity.

• The Content Provider may encode their content in multiple forms

Video Quality

Level

Video (kbps) Total (kbps) Comments

VQ1 200 296 Smartphone

VQ2 400 496 Tablet / Smartphone

VQ3 800 896 Tablet / Smartphone

VQ4 1400 1496 SD 480P TV screen through games console and

STBs, Connected TVs, PCs and Tablets

VQ5 3150 3246 HD 72P TV screen through games console and

STBs, Connected TVs, PCs and tablets

VQ6 7100 7196 Full HD 1080P, TV screen through games console

and STB, Connected TVs, PCs with General

Processor Units.

Video Quality

Level

Video (kbps) Total (kbps) Comments

VQ1 200 296 Smartphone

VQ2 400 496 Tablet / Smartphone

VQ3 800 896 Tablet / Smartphone

VQ4 1400 1496 SD 480P TV screen through games console and

STBs, Connected TVs, PCs and Tablets

VQ5 3150 3246 HD 72P TV screen through games console and

STBs, Connected TVs, PCs and tablets

VQ6 7100 7196 Full HD 1080P, TV screen through games console

and STB, Connected TVs, PCs with General

Processor Units.

Recommendations for video application developers

• Both the video delivery algorithm and the network configuration must be optimized.

– Video application developers need to consider the challenges in operating over a network with mobile devices.

– Applications must be adaptable to changing access capabilities, and access types.

• Utilize APIs between the Application Layer and Access

– Drive the creation of APIs to demand QoS from the network

– Mobile operators could provide the development space and environment for realistic simulations

• Chunk size: Ensure that video servers deliver chunks of video in small regular intervals ~10 seconds.

– The most optimal value of chunk size depends on, amongst other things, the eNodeB timer value settings. Video sources with significant inactivity periods between chunks of data can waste signaling resources and device battery.

• Ensure that polling requests from the devices to the network are bundled to maximize use of seized resources.

Summary & Conclusions

• For Operators

– Architecture choices and improvements in both access and content.

– Work smartly with available and forecasted mobile broadband.

• For Vendors

– Areas for cooperation, integration in content generation and delivery

– Improvement and optimization in devices and networks.

• For Application developers and architects

– Optimize design of video applications to minimize effect on mobile

networks

• For Industry analysts

– The state of video on mobile broadband is described, trends identified.

• For Device manufacturers

– Evolution in video processing technology and codecs.

Thank you