Designing for Exponential Growth in Mobile Video Traffic ... 15, 2012 Gibson 1 1 Designing for...

June 15, 2012 1Gibson 1

Designing for Exponential Growth in Mobile Video Traffic:

Applications, Codecs, Networks, and Quality

Jerry D. GibsonDepartment of Electrical and Computer

EngineeringUniversity of California, Santa Barbara

June 15, 2012 22

Why We Are Here

Gibson 2


Dealing with Exponential Growth

• In information theory and rate distortion theory, the number of codewords for a blocklength N

and

rate R bits/dimension grows exponentially in N and R

• Leading Wozencraft and Jacobs [1965] to declare, “One cannot trifle with exponential growth.”


Key Points in Developing a Solution

• “If you don’t have command of the full technology chain, and you provide a solution, you are probably providing the wrong solution”

• “You should not develop a solution that has only one application”

• “The solution should not be too disruptive”Umesh Mishra 3/22/2012 NAE Regional Meeting, UCSB

June 15, 2012 55

Video over Wireless Networks• Video applications over wireless networks

– Mobile video streaming– Video calls– Remote health care– Wireless video surveillance– Video for military– Many more


How Many Video Applications Do We Design For?

Why the future of business is selling lessof more


The Long Tail of ApplicationsThe 'Long Tail of Applications'

Number of Applications

Number of Users

Many service choices, low usage patterns

Few service choices, high usage patterns


QCI Resource type

Priority Packet delay budget (ms)

PER Example services

1 GBR 2 100 10-2 Conversational voice

2 GBR 4 150 10-3 Conversational video (live streaming)

3 GBR 5 300 10-6 Non-conversational video (buffered

streaming)4 GBR 3 50 10-3 Real time gaming

LTE QOS Class Identifiers

June 15, 2012 9

QOS Class Identifiers (cont’d)QCI Resource

typePriority Packet delay

budget (ms)PER Example

services5 Non-GBR 1 100 10-6 IMS signaling 6 Non-GBR 7 100 10-3 Voice, video (live

streaming), interactive gaming

7 Non-GBR 6 300 10-6 Video (buffered streaming)

8 Non-GBR 8 300 10-6 TCP-based (e.g. WWW, e-mail) chat, FTP, p2p

file sharing, progressive video, etc.

9 Non-GBR 9 300 10-6


2001 Book

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Multimedia Communications Network (1998-2001)

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Elements of Today’s Wireless Video Networks

• Wireless LANs• WiMax (not so much in the U.S.)• Cellular, especially LTE• Ad hoc and mesh networks• Maybe 60 GHz Networks

(Download or upload stations)?Gibson 13


60 GHz Networks (Madhow, UCSB)

June 15, 2012 1616

Projection from 2001

0

50

100

150

200

250

300

1997 2002 2007 2012

Growth: 10% telephone, 30% Internet

Hypothesis 3:The Digital Video Age

TelephoneInternetDigital Video


100 Terabit Ethernet by 2020 (1 Terabit by 2015)

Blumenthal, UCSB

June 15, 2012 18


The emerging world of the wireless Web consists of a wide variety of fixed and mobile clients connected to the Internet over a wide range of link speeds, all trying to access multimedia content stored on a remote

Web server or database. (CDPD=Cellular Digital Packet Data; DSL=Digital Subscriber Line.)


Proxy-based transcoding of multimedia content involves on- the-fly transformations of a server’s content by an

intermediate proxy for presentation to diverse clients connected to the Internet over diverse access links.


End-to-end transcoding generates multiple versions of a multimedia document prior to a client request. Each version is adapted for a specific

client and access link. In response to a client request, the server returns the appropriate pretranscoded version, thereby avoiding on-the-fly

transcoding by a proxy


A chain of transcoding proxies can lead to an accumulation of noise, additive delay, and

heightened security risks

June 15, 2012 23

Internal architecture of a Web transcoding proxy

Decode &

Analyze

Text

ImagesTranslate

& Compress

Modified Text/HTMLTranscoded ImageInfoPyramid Data

Representation Transformation Modules

Adaptive Transcoding Policies: When, what and how much to transcode

Server-to-Proxy Bandwidth

Client/Device Capabilities

User Preferences

Proxy-to-Client BandwidthHTML

& images

Web Server InternetTranscoding

HTTP ProxyWeb

ServerInternet


End-to-end Serving of Pretranscoded Web Pages vs. Proxy-Based Real-Time

Properties End-to-End Serving of Pretranscoded Content

Proxy-Based Real-Time Transcoding

Modifications to existing infrastructure

Upgrade servers to generate multiple pretranscoded versions of a Web page, server informed of client characteristics

No modification at server, client redirected to point to proxy, client needs to advertise its characteristics to proxy

Latency Little additional delay Compression reduces delay, but compute-transcoding adds delay

Scalability in terms of processing

Offline transcoding can be performed when convenient

Highly compute-intensive transcoding, often under real-time deadline pressure


End-to-end Serving of Pretranscoded Web Pages vs. Proxy-Based Real-Time cont’d…

Properties End-to-End Serving of Pretranscoded Content

Proxy-Based Real-Time Transcoding

Security Supports end-to-end encryption

Proxy must be trusted to decrypt, decompress, recompress, and re-encrypt

Semantic understanding

Server knows semantic importance of each media object in a page or document

Proxy has incomplete knowledge of page composer’s intents; proxy can be aided by hints from server


End-to-end Serving of Pretranscoded Web Pages vs. Proxy-Based Real-Time cont’d…Properties End-to-End Serving of

Pretranscoded ContentProxy-Based Real-Time

Transcoding

Degradation No additional degradation

Noise/analog degradation accumulates with each decompression-lossy compression cycle

Legacy support Backward compatibility may be limited

Transitional role supporting legacy client hardware and compression standards, phased introduction of new standards

TCP semantics Unaffected Often broke by a proxy


LTE UE Categories

1 2 3 4 5

Maximum downlink data rate (Mbps) 10 50 100 150 300Maximum uplink data rate (Mbps) 5 25 50 50 75Number of receive antennas required 2 2 2 2 4Number of downlink MIMO streams supported 1 2 2 2 4Support for 64QAM modulation in downlink √ √ √ √ √Support for 64QAM modulation in uplink × × × × √Relative memory requirement for physical layer processing (normalize to category 1 level

1 4.9 4.9 7.3 14.6

UE Category


QoE metrics and reporting framework for 3GPP DASH and

progressive download [1]

Multimedia content

QoE activation trigger HTTP server

QoE report

3GPP client

UE eNB

Wireless network IP network

Public network (internet)

Core network

Access network

QoE reporting

server

QoE monitor


LTE and Digital Cellular

• Base Station/eNodeB– All powerful– Total number of users optimization rather

than QoE per user– QoE not dominant– Base station/eNodeB actions not

standardized


Three Rules for Enhancing Video Over Wireless Networks

• All video over wireless networks applications are joint source/channel coding problems

• Performance evaluations should be statistical across a network

• To improve performance, use everything you know


Outage rates, No CSI at Transmitter [2-6]


Expected Distortion, No CSI at Transmitter [2-6]


pd

, pr

and Respective Distortions for the Various Strategies with =10db

[2-6]pd pr D (pd

) D (pr

) ΔD%ALM 0.015 0.130 0.0458 0.1372 199.41

TS-MD-OPT 0.084 0.212 0.0825 0.1058 28.24REP 0.026 0.108 0.1069 0.1551 45.01SM 0.013 0.157 0.0338 0.1589 369.76

γ


CDF of the distortion for different strategies at a fixed of 5dB—CSI at

Transmitter [2-6]γ





• To improve performance, use everything you know


PSNR per Video Frame and Realization [7-9]

• Silent.cif coded at GOPS = 15, PS = 100 for 100 realizations of multipath fading channel of average SNR 7 dB when PHY data rate 6 Mbps is used (Thick lines are avg PSNR across realizations)

(a) QP = 26, fading@7dB, avgPER = 5.5% (b) QP = 26,AWGN@3dB, avgPER = 1.5%


Comparing PSNRr,f for different QPs and channel conditions [7-9]

• Multipath fading channel of average SNR 7 dB and AWGN channel of SNR 3 dB respectively

• Silent.cif, with fixed r=85%, PHY data rate = 6 Mbps, PS = 100, GOPS = 15 (Avg PSNR is calculated across all frames of all realizations)


• Key idea– Routing messages Frame corruption estimation

Reference frame selection

Routing-aware MDC [10-13]

Wireless networks

Routing-aware Multiple Description

Video Encoder

Routing-aware Multiple Description

Video Encoder

Multipath Routing Protocol

Multipath Routing Protocol

Packet/Frame Loss Estimation Packet/Frame

Loss EstimationReference frame selection

Routing messages

Description 1

Description 2

June 15, 2012 41

Routing Message Based Loss Estimation [10-13]

V

1

V

2

V

3

V

4

V

5

V

6

V

7

V

8

RERR

V

1

V

2

V

3

V

4

V

5

V

6

V

7

RREQ RREP

RERR

Source

Node

time

Intermediate

Node

time

What happened to these packets?

GOODsuccess

BADunreliable

FAILlost

Need to estimate packet loss rate of each packetNeed to estimate packet loss rate of each packet

…

RERR: route error RREQ: route request RREP: route reply

June 15, 2012 42

State Probability Distribution pg

(n), pf

(n), pb

(n)

[10-13]

• State depends on when route failure occurs– Estimate the overall delay Tdelay

⎪⎩

⎪⎨

⎧

>=

−>≥=

−≤=

)()(

))1(()(

))1(()(

datadelayb

datadelaydataf

datadelayg

nTTpnp

TnTnTpnp

TnTpnp

RERR

V

1

V

2

V

3

V

4

V

5

V

6

V

7

RERR RREQSource

Node

time

V

1

V

2

V

3

V

4

V

5

V

6

V

7

Intermediate

Node

time

GOOD BADFAIL

Tdelay

Tdata


Foreman (CIF, 15 fps), 400 kbps [10-13]


Conclusions

• Understand the full technology chain • Develop solutions for the Long Tail of

Applications• The solution should not be too

disruptive—do not expect too much cooperation


References[1] O. Oyman and S. Singh, “Quality of Experience for HTTP Adaptive Streaming Services,” IEEE Communications Magazine, April 2012. [2] S. Choudhury and J. D. Gibson, "Ergodic Capacity, Outage Capacity, and Information Transmission over Rayleigh Fading Channels," Information Theory and Applications Workshop, University of California, San Diego, La Jolla, CA, January 29-Feb 2, 2007.[3] S. Choudhury and J. D. Gibson, "Information Transmission over Fading Channels," IEEE Global Communications Conference, November 26-30, 2007.


References[4] M. Zoffoli, J. D. Gibson and M. Chiani, “Rate-Adaptive Information Transmission over MIMO Channels,” MIMO Systems, Theory and Applications, Feb. 2011.[5] M. Zoffoli, J. D. Gibson, and M. Chiani, “On Strategies for Source Information Transmission over MIMO Systems,” IEEE Global Telecommunications Conference 2008 (GLOBECOM '08), November 30 - December 4, 2008.[6] M. Zoffoli, J. D. Gibson, and M. Chiani, "Source Information Transmission over MIMO Systems with Transmitter Side Information," 46th Annual Allerton Conference on Communications, Control, and Computing, Monticello, IL, September 23-26, 2008.


References[7] J. Hu, S. Choudhury and J. D. Gibson, “Video Capacity of WLANs with a Multiuser Perceptual Quality Constraint,” IEEE Trans. on Multimedia, Vol. 10, No. 8, pp. 1465-1478, 2008. [8] J. Hu and J. D. Gibson, "New Rate Distortion Bounds for Natural Videos Based on a Texture Dependent Correlation Model," IEEE International Symposium on Information Theory, Nice, France, June 24-29, 2007.[9] J. Hu, S. Choudhury and J. D. Gibson, “PSNR_{r,f}- MOS_r: An Easy-To-Compute Multiuser Perceptual Video Quality Measure,” QoMEX, First International Workshop on Quality of Multimedia Experience, San Diego, CA, July 29- 31, 2009.


References[10] Y. Liao and J. D. Gibson, “Routing-aware Multiple Description Coding with Multipath Transport for Video Delivered over Mobile Ad-hoc Networks,” Proceedings of The First IEEE Workshop on Multimedia Communications and Services (MCS), Dec. 2010. [11] Y. Liao and J. D. Gibson, “Routing-aware Multiple Description Video Coding over Wireless Ad-hoc Networks using Multiple Paths,” ICIP 2010, Hong Kong, September 2010. [12] Y. Liao and J. D. Gibson, “Video communications over wireless ad-hoc networks using source coding diversity and multiple paths,” Mobile Ad-Hoc Networks: Protocol Design, Dec. 2010.

June 15, 2012 49

References[13] Y. Liao and J. D. Gibson, “Routing-aware Multiple Description Video Coding Over Mobile Ad-Hoc Networks,” IEEE Transaction on Multimedia, Vol. 13, No. 1, pp. 132- 142, Feb. 2011.

Gibson 49

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