Transport of RealTime Flows

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Transport of Real-Time Flows

• Bernd Girod
• Information Systems Laboratory
• Stanford University

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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 . . . .
. . Ultimatelyperhaps by 2010voice may become a
free internet application, with operators making
money from related internet applications like
IPTV . . .
Economist, September 2005
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IPTV Rollout
Verizon 10M householdsby 2009
IPTV SBC18M households by 2007
IEEE Spectrum, Jan. 2005
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Why Is Real-Time Transport Hard?
Internet is a best-effort network . . .
Congestion Insufficient rate to
communicate Packet loss Impairs perceptual
quality Delay Impairs interactivity of
services Telephony one way delay ITU-T Rec. G.114 Delay
jitter Obstructs continuous media playout
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Clean Slate Internet DesignResearch Areas
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Outline of the Talk

• QoS vs. best effort
• Resource allocation for IPTV
• Rate-distortion optimized streaming

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How 1B Users Share the Internet
Rate r
TCP Throughput
RTT
maximum transfer unit
data rate
Growing congestion
p
packet loss rate
0.001
0.0001
0.1
0.01
round trip time

• Mahdavi, Floyd, 1997
• Floyd, Handley, Padhye, Widmer, 2000

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QoS vs. Best Effort

• Reservation-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
  its cheaper than QoS guarantees

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Simple 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
Breslau, Shenker, 1998
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Rigid Applications
u

• Utility u0 below of minimum bit-rate B
• Maximum total utility Uk is achieved by
  admitting at most k flows

1
C/k
B
Breslau, Shenker, 1998
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Rigid Applications (cont.)

• Expected loss in total utility w/o admission
  control
• Gap DU is substantial when number of admissable
  flows k is small
• Gap DU usually disappears with growing capacity
  C? Overprovisioning can solve the problem!

Breslau, Shenker, 1998
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Elastic 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!

u
C/k
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Video 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

Good picture quality
Foreman Mobile
Bad picture quality
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Different Utility Functions

• Example uk(rk)1-akrk
• With rk0 ? Karush-Kuhn-Tucker conditions
  (reverse water-filling)
• Better than utility-oblivious fair sharing

Equal-slope Pareto condition
uk
Vilfredo Pareto 1848-1923
rk
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Distribution of IPTV over WLAN
5 Mbps
11 Mbps
2 Mbps
Home Media Gateway
courtesy van Beek, 2004
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Video Streaming Over Shared Channel
Transcoder
Decoder
0
0
Transcoder
Decoder
1
1
Receiver
Transcoder
Decoder
(Multi-Channel)
2
2
Transcoder
Decoder
3
3
Controller
Kalman, van Beek, Girod 2005
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Dynamically Changing RD Function
Scene Cut
Weighted average of last I-Frame, P-Frame and
B-Frame used for RD estimation
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Utility-Based Resource Allocation
Min-MSE Allocation
Equal Time Allocation

• Average PSNR 37.6 dB
• Average PSNR Stream 4 35.5 dB

• Average PSNR 37.5 dB
• Average PSNR Stream 4 32.9 dB

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Tx Backlog for 4 Video Streams 85 WLAN
Utilization
Kalman, van Beek, Girod 2005
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Streaming of Stored Content
Media files are already compressed How can we
nevertheless adapt to network?
100s to 1000ssimultaneous streams
DSL
Cable
wireless
Server
Network
Client
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Not All Packets are Equally Important
I
I
P
P
I
B
B
B
P
P
P
I
B
B
B
P



A
A

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Not All Packets are Equally Important
I
I
P
B
P
P
I
B
B
P
P
I
B
B
B
P



A
A

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Distortion-Aware Packet Dropping
Good Picture quality
Distortionaware
Packet dropping No retransmissions QCIF
Carphone I-P-P-P-P-P- . . .
Bad picture quality
Oblivious
Percentage of Packets Retained
Chakareski, Girod, ICME 2004
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Rate-DistortionOptimized (RaDiO) Streaming

• Decide which packets to send (and when) to
  maximize picture quality while not exceeding an
  average rate 2001

Server
Client
Network
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A Brief History of Media Streaming

• Media streaming w/o congestion avoidance
  reckless driving 1995
• TCP-friendly rate control Limit average rate
  for fair sharing with TCP 1997
• Rate-distortion optimized packet scheduling
  (RaDiO) Decide which packets to send (and
  when) to maximize picture quality while not
  exceeding an average rate 2001
• Congestion-distortion-optimized
  scheduling/routing (CoDiO) Decide which
  packets to send (and when) to maximize picture
  quality while minimizing network congestion.
  2004

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Congestion 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

Rmax
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Video Distortion with Self Congestion
Good Picture quality
Bad picture quality
Bit-Rate kbps
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Streaming with Last Hop Bottleneck
Random cross traffic
High bandwidth links
Video traffic
Low bandwidth last hop
Acknowledgments
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Delay distribution

• Overall delay distribution
• Queue length determines delay of last hop

pdf
delay
C
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Comparison RaDiO vs. CoDiO
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PSNR dB
PSNR dB
Rate kbps
End-to-end delay ms
Simulations using H.263 Rate 10 fps Sequence
Foreman (32kbps,32kbps) Sequence length
60s Playout deadline 600ms
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Concluding Remarks

• Over-provisioning makes QoS superfluous
• Elastic applications dont need QoS
• Joint rate control for access bottlenecks (e.g.
  IPTV, WLAN)
• Media-aware congestion control (e.g. CoDiO)

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The End

• http//www.stanford.edu/bgirod/publications.html