transport of real-time traffic over the internet bernd girod information systems laboratory stanford...

Transport of Transport of Real-Time TrafficReal-Time Trafficover the Internetover the Internet

Transport of Transport of Real-Time TrafficReal-Time Trafficover the Internetover the Internet

Bernd GirodBernd Girod

Information Systems LaboratoryInformation Systems LaboratoryStanford UniversityStanford University

22B. Girod: Internet Real-Time Transport, September 2005

[Economist, September 2005]

THE MEANING OF FREE SPEECH

The acquisition by eBay of Skype is a helpful reminder to the world's trillion-dollar telecoms industry that all phone calls will eventually be free . . .

. . . Ultimately—perhaps by 2010—voice may become a free internet application, with operators making money from related internet applications like IPTV . . .

THE MEANING OF FREE SPEECH

The acquisition by eBay of Skype is a helpful reminder to the world's trillion-dollar telecoms industry that all phone calls will eventually be free . . .

. . . Ultimately—perhaps by 2010—voice may become a free internet application, with operators making money from related internet applications like IPTV . . .


IPTV Rollout IPTV Rollout

IPTV SBC18M householdsby 2007

IPTV SBC18M householdsby 2007

Verizon10M households

by 2009

Verizon10M households

by 2009

[IEEE Spectrum, Jan. 2005]


Why Is Real-Time Transport Hard?Why Is Real-Time Transport Hard?

Internet is a best-effort network . . .

Congestion Insufficient rate to communicatePacket loss Impairs perceptual qualityDelay Impairs interactivity of services;

Telephony: one way delay < 150 ms [ITU-T Rec.

G.114]

Delay jitter Obstructs continuous media playout


Outline of the TalkOutline of the Talk

• QoS vs. best effort

• Resource allocation for IPTV

• Rate-distortion optimized streaming

• Multi-path routing

• P2P multicasting of live video streams


1.22 MTUr

RTT p

1.22 MTUr

RTT p

How 1B Users Share the InternetHow 1B Users Share the Internet

maximum transfer

unit

roundtrip time

packetloss rate

data rate

[Mahdavi, Floyd, 1997]

[Floyd, Handley, Padhye, Widmer, 2000]

Rate r

Growing congestion

p0.0010.0001 0.10.01

TCP Throughput


QoS vs. Best EffortQoS vs. Best EffortReservation-ism

– Voice and video need guaranteed QoS (bandwidth, loss, delay)

– Implement admission control: “Busy tone” when network is full

– Best effort is fine for data applications

Best Effort-ism– Best Effort good enough for

all applications– Real-time applications can

be made adaptive to cope with any level of service

– Overprovisioning always solves the problem, and it’s cheaper than QoS guarantees


Simple Model of A Shared LinkSimple Model of A Shared Link• Link of capacity C is shared among k flows

• Fair sharing: each flow uses data rate C/k• Homogeneous flows with same utility function u(.)• Total utility

C

CU k k u

k

[Breslau, Shenker, 1998]


Rigid ApplicationsRigid Applications• Utility u=0 below of

minimum bit-rate B

• Maximum total utility U=k* is achieved by admitting at most k* flows

u

C/k

* arg maxk

C Ck k u

k B

B

1



Rigid Applications (cont.)Rigid Applications (cont.)• Expected loss in total utility w/o admission control

• Gap U is substantial when number of admissable flows k* is small

• Gap U usually disappears with growing capacity C Overprovisioning can solve the problem!

PrC C

U kB B



Elastic ApplicationsElastic Applications• Elastic applications: utility function u(k), such

that total utility U(k)=ku(C/k) increases with k• Example:

u(C/k)=1-aC/k

• All flows should be admitted: best effort!

C/k

u


0 500 1000 1500 2000 2500 3000 3500 400024

26

28

30

32

34

36

38

40

42

44

Y-PS

NR

in d

B

encoding rate in kbps

mobile

foreman

Video CompressionVideo Compression• H.264 video coding for 2

different testsequences• Video is elastic application• Rate must be adapted to

network throughput• How to achieve rate control

for stored content or multicasting?

• Utility function depends on content: should use unequal rate allocation Foreman

Mobile

Goodpicturequality

Badpicturequality


• Example: uk(rk)=1-akrk

• With rk>=0 Karush-Kuhn-Tucker conditions (“reverse water-filling”)

• Better than utility-oblivious “fair” sharing

Different Utility FunctionsDifferent Utility Functions

rk

uk

Equal-slope “Pareto condition”

Vilfredo Pareto1848-1923


Distribution of IPTV over WLANDistribution of IPTV over WLAN

[courtesy: van Beek, 2004]

5 Mbps

2 Mbps

11 Mbps

Home MediaGateway


Receiver

(Multi-Channel)

Transcoder

Transcoder

Transcoder

Transcoder

0

1

2

3

Decoder

Decoder

Decoder

Decoder

0

1

2

3

Controller

Video Streaming Over Shared ChannelVideo Streaming Over Shared Channel

[Kalman, van Beek, Girod 2005]


0 10 20 30 400

2

4

6

8

10

ba

cklo

g in

fra

me

s

0 10 20 30 400

2

4

6

8

10

0 10 20 30 400

2

4

6

8

10

time in seconds

ba

cklo

g in

fra

me

s

0 10 20 30 400

2

4

6

8

10

time in seconds

Tx Backlog for 4 Video Streams Tx Backlog for 4 Video Streams 85% WLAN Utilization85% WLAN Utilization

[Kalman, van Beek, Girod 2005]


Streaming of Stored ContentStreaming of Stored Content

DSL

Cable

wireless

Media files are already compressed:How can we nevertheless adapt to network?

100s to 1000ssimultaneousstreams

Server ClientNetwork


Not All Packets are Equally ImportantNot All Packets are Equally Important

P PI

I

B B B P P PI

I

B B B P

A

…

…

…

A …


PBP PI

I

B B P PI

I

B B B P

A

…

…

…

A …

Not All Packets are Equally ImportantNot All Packets are Equally Important


Distortion-Aware Packet DroppingDistortion-Aware Packet DroppingGoodPicturequality

Badpicturequality

Percentage of Packets Retained [%]

Distortionaware

Packet droppingNo retransmissionsQCIF CarphoneI-P-P-P-P-P- . . .Oblivious

[Chakareski, Girod, ICME 2004]


Rate-DistortionRate-DistortionOptimized (RaDiO) StreamingOptimized (RaDiO) Streaming

“Decide which packets to send (and when) to maximize picture quality while not exceeding an average rate” [2001]

Server Client

Request stream

Rate-distortionpreamble

Packetschedule

Video data

RepeatrequestRepeatrequestRepeatrequest

Network


A Brief History of Media StreamingA Brief History of Media Streaming

1) Media streaming w/o congestion avoidance: “reckless driving” [1995]

2) TCP-friendly rate control: “Limit average rate for fair sharing with TCP” [1997]

3) Rate-distortion optimized packet scheduling (RaDiO): “Decide which packets to send (and when) to maximize picture quality while not exceeding an average rate” [2001]

4) Congestion-distortion-optimized scheduling/routing (CoDiO): “Decide which packets to send (and when) to maximize picture quality while minimizing network congestion.” [2004]


Congestion vs. RateCongestion vs. Rate• Congestion: queuing delay that packets experience

– weighted by size of the packet– averaged over all packets in the network

• Congestion increases nonlinearly with link bit-rate

Congestion [seconds]

Rate R

max

Example: M/M/1 model

1 =

R -R

max

Example: M/M/1 model

1 =

R -R

Rmax


Video Distortion with SelfVideo Distortion with Self CongestionCongestion

GoodPicturequality

Badpicturequality

Bit-Rate [kbps]

Self congestioncauses late loss


Streaming with Last Hop BottleneckStreaming with Last Hop Bottleneck

Random cross traffic

Low bandwidth last hop

Video traffic

Acknowledgments

High bandwidth links


Delay distributionDelay distribution

• Overall delay distribution

• Queue length determines delay of last hop

delay

pdf

C


Comparison RaDiO vs. CoDiOComparison RaDiO vs. CoDiO

Simulations using H.263+

Rate : 10 fps

Sequence : Foreman (32kbps,32kbps)

Sequence length : 60s

Playout deadline : 600ms

50 %

PS

NR

[dB

]

Rate [kbps]P

SN

R [

dB]

End-to-end delay [ms]


How To Avoid Traffic Jams?How To Avoid Traffic Jams?

• Avoid congested times . . .Congestion-aware packet

scheduling

• Avoid congested roads . . . Congestion-aware routing


Multipath Routing for Minimum CongestionMultipath Routing for Minimum Congestion

7716kbps

25

15

718

35

222

8

23 238

69

4364 24

24

31 kbps

45

24

Mesh network, fully connected Streaming 100 kbps from Node 1 to Node 5 Random cross traffic


Multipath Video StreamingMultipath Video Streaming

6 dB

Sequence : Foreman QCIF, 250 frames, 30 fps

Codec: H.26L TML 8.5

Playout deadline : 500 ms

Packetization : 1 frame/packet

Traffic model: CBR

No. of realizations: 400

GoodPicturequality

Badpicturequality

Bit-Rate [kbps]


Multipath Video Streaming

1 path80 kbps, PSNR 32.5 dB

3 paths187 kbps, PSNR 36.2 dB


Distribution of Live Streams Distribution of Live Streams via “Pseudo-Multicast”via “Pseudo-Multicast”

ExampleAOL webcast of Live 8 concert

July 2, 2005

Content delivery network

. . . . . . . . . . . . . . . . . .

Splitterservers

Mediaserver

1500 servers in 90 locations

50 Gbps

175,000 simultaneous viewers

8M unique viewers


P2P live multicast

Content delivery network

. . . . . . . . . . . . . . . . . .

Splitterservers 1500 servers in 90 locations

50 Gbps

Distribution of Live Streams Distribution of Live Streams via “Pseudo-Multicast”via “Pseudo-Multicast”

ExampleAOL webcast of Live 8 concert

July 2, 2005

Mediaserver

175,000 simultaneous viewers

8M unique viewers

300 kbps


P2P Multicast over 1 TreeP2P Multicast over 1 Tree


P2P Multicast over 2 TreesP2P Multicast over 2 Trees


P2P Ungraceful Parent LeaveP2P Ungraceful Parent Leave

3 treesParent of yellow tree is down

Hello, Yellow Tree

Parent?

Parent leave is detected

Retransmissions requested

New parent is selected

Yellow tree is recovered


Experimental Set-upExperimental Set-up• Network/protocol simulation in ns-2

– 1000 nodes– 300 active peers – Random peer arrival/departure:

ON (5 min)/OFF (30 s) – Over-provisioned backbone– Typical access bandwidth distribution– Delay: 5 ms/link + congestion

• Video streaming– Compression H.264 at 220 kbps– 15 minute live multicast

[Setton, Noh, Girod, ACM MM 2005]


Join and Rejoin LatenciesJoin and Rejoin Latencies



Congestion-DistortionCongestion-DistortionOptimized P2P Live StreamingOptimized P2P Live Streaming

% peersconnected to 4/4 trees

% peersconnected to 4/4 trees


With CoDiO

Without CoDiO


Congestion-distortion optimized (CoDiO) streaming

Without CoDiO

P2P Video Multicast: 64 out of 300 Peers

H.264 @ 220 kbps2 sec latency for all streams


Concluding RemarksConcluding Remarks• Over-provisioning makes QoS superfluous• Elastic applications don’t need QoS• Joint rate control for access bottlenecks

(e.g. IPTV, WLAN)• Media-aware congestion control (e.g. CoDiO)• Multipath routing to mitigate congestion• P2P viable alternative for content delivery

networks

Client-server edge-based P2P

The EndThe EndThe EndThe Endhttp://www.stanford.edu/~bgirod/publications.htmlhttp://www.stanford.edu/~bgirod/publications.html

transport of real-time traffic over the internet bernd girod information systems laboratory stanford...

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