How to Meet the Deadline for Packet Video

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How to Meet the Deadlinefor Packet Video

• Bernd Girod
• Mark Kalman
• Eric Setton
• 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 is Becoming a Reality
Verizon 10M IPTV households by 2009
SBC (ATT) 18M IPTV households by 2007
IEEE Spectrum, Jan. 2005
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Why Is Internet Video Hard?
Internet is a best-effort network . . .
Congestion Insufficient rate to carry all
traffic Packet loss Impairs perceptual
quality Delay Impairs interactivity of
services Zapping lt 500 ms
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How to Meet the Deadline for Packet Video
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How to Meet the Deadline for Packet Video
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How to Meet the Deadline for Packet Video

• Congestion, QoS, and fair sharing
• Maximum-utility resource allocation for multiple
  video streams
• Example video over wireless home networks
• Congestion-distortion optimized packet scheduling
  (CoDiO)
• Example P2P multicasting of live video
• Packet scheduling for multicast trees

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Measuring Congestion
EDelay Congestion
Traffic flow

• Congestion in packet-switched network
• queuing delay that packets experience,
• weighted by size of the packet
• averaged over all packets in the network

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Congestion GrowsNonlinearly with Link Utilization
Congestion D seconds
C
Rate R
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How 1B Users Share the Internet
Rate R
TCP Throughput
maximum transfer unit
Growing congestion
data rate
packet loss rate
p
round trip time
0.001
0.0001
0.1
0.01

• 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)
• Requires 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 admitted flow uses rate RC/k
• Homogeneous flows with same utility function u(R)
• Total utility

C
Breslau, Shenker, 1998
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Rigid Applications
u

• Utility u0 below of minimum bit-rate B
• Admit at most flows
• With sufficient overprovisioning, no admission
  control needed, since

1
C/k
B
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Elastic Applications
u(R)

• Elastic applications convex utility function
  u(R)
• All flows should be admitted best effort!

R
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QoS vs. Best Effort for Video

• H.264 video coding for 2 different testsequences

• Video is elastic application . . . above a
  certain minimum quality
• Bottleneck links admission control and dynamic
  rate control combined
• Rate must be adapted to network throughput. How?
• 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

• Better than utility-oblivious fair sharing
• With rkgt0 ? Karush-Kuhn-Tucker conditions

uk
Equal-slope Pareto condition
Vilfredo Pareto 1848-1923
rk
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Distribution of TV over WLAN
5 Mbps
11 Mbps
2 Mbps
Home Media Gateway
courtesy van Beek, 2004
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Video over WLAN
Network Interface
playout buffer
802.11b
Transcoder
Receiver
Decoder
Video encoded at higher rate
Wireless Terminal
Controller
Kalman, van Beek, Girod 2005
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Video over WLAN with Multiple Streams
Transcoder
Decoder
0
c0
0
Network Interface
Transcoder
Receiver

1
Decoder
c1
1


(Multi-Channel)

Transcoder
M
cM
Decoder
M
Controller
Wireless terminals
Kalman, van Beek, Girod 2005
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Dynamic Estimation of R-D Curve
Scene cuts
R-D Model
Stuhlmüller et al. 2000
Parameters track weighted average of last
I-Frame, P-Frame and B-Frame
Rate ?
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802.11b Transmission of 2 Video Streams
Link rates kbps
Channeltimeallocation
Transcoderbit-ratekbps
Backlogin frames
PSNRin dB
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Video Distortion with Self Congestion
Good Picture quality
Bad picture quality
Bit-Rate kbps
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Effect of Playout Delay and Loss Sensitivity
Foreman
Salesman
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40 headroom
Simulations over ns-2 Link capacity 400 kb/s
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1 sender
Simulation of 600 kbps link Latency 400 msec
380 kbps, 36 dB Highest sustainable video quality
420 kbps, 33.7 dB
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Modeling Self-Congestionfor Packet Scheduling

• Rate-distortion optimized packet scheduling
  (RaDiO) typically assumes independent delay pdfs
  for successive packet transmissions Chou, Miao,
  2001
• Model delay pdf by exponential with varying shift

Probability distribution
delay
Setton, Girod, 2004
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CoDiO Light Scheduler
I
B
B
B
P
Pictures to send
Schedule
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CoDiO Scheduling Performance
Mother Daughter
News
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Playout deadline (s)
Playout deadline (s)
Simulations over ns-2 Packet loss rate
2 Bandwidth 400 kb/s Propagation delay 50ms
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CoDiO
ARQ
H.264/AVC _at_250 kb/s Link rate 400 kb/s,
propagation delay 50 ms 2 packet loss0.6
second playout deadline
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CoDiO vs. RaDiO
Playout deadline (s)
Playout deadline (s)
Sequence Foreman Packet loss rate 2 Link
capacity 400 kb/s Propagation delay 50ms
Playout deadline (s)
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Video Multicast over P2P Networks

• Challenges
• Limited bandwidth
• Delay due to multi-hop transmission
• Unreliability of peers
• Our Approach Setton, Noh, Girod, 2005
• Determine encoding rate as a function of network
  bandwidth
• Build and maintain complementary multicast trees
• Adapt media scheduling to network conditions and
  to content
• Request retransmissions to mitigate losses
• Related work
• Chu, Rao, Zhang, 2000
• Padmanabhan, Wang and Chou, 2003
• Guo, Suh, Kurose, Towsley, 2003
• Cui, Li, Nahrstedt, 2004
• Do, Hua, Tantaoui, 2004
• Hefeeda, Bhargava, Yau, 2004
• Zhang, Liu, Li and Yum, 2005
• Zhou, Liu, 2005
• Chi, Zhang, Packet Video 2006

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Experimental Setup

• Network/protocol simulation in ns-2
• 300 active peers
• Random peer arrival/departure average life-time
  5 minutes
• Over-provisioned backbone
• Typical access rate distribution
• Delay 5 ms/link congestion
• Video streaming
• H.264/AVC encoder _at_ 250 kb/s
• 15 minute live multicast

Sripanidkulchai et al., 2004
Setton, Noh, Girod, 2005
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Join and Rejoin Latencies
Simulations over ns-2, 300 peers Number of trees
4 Retransmissions enabled
Setton, Noh, Girod, 2005
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P2P Video Multicast 64 out of 300 Peers
CoDiO retransmissions
No retransmissions
H.264 _at_ 250 kb/s2 second playout deadline for
all streams
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P2P Video Multicast 64 out of 300 Peers
CoDiO retransmissions
No retransmissions
H.264 _at_ 250 kb/s2 second playout deadline for
all streams
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CoDiO Scheduling for Multicast Trees
Child
Parent
P
I
B
P
P
B
B
Child
DI
DP3
DB
DB
DP2
DP1
DB
Setton, Noh, Girod, 2006
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Gain by Multicast CoDiO
Foreman
Mother Daughter
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40
Playout deadline (s)
Playout deadline (s)
Simulations over ns-2, 300 peers Number of
trees 4 Retransmissions enabled
Setton, Noh, Girod, 2006
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Average Video Sequence for 75 Peers
Sender-driven CoDiO light 33.71 dB
Without prioritization 30.17 dB
H.264 _at_ 250 kb/s0.8 second playout deadline for
all streams
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Conclusions

• Must avoid congestion for low latency
• Video streaming over bottlenecks (IPTV, WLAN . .
  . )combine admission control and rate control
• R-D-aware rate allocation better than fair
  sharing
• Packet scheduling should consider congestion
  rather than rate
• Low-complexity CoDiO scheduler
• P2P video multicast possible with low latency
• Retransmissions effective with application-layer
  multicast
• CoDiO extended to packet scheduling for multicast
  trees
• Cross-layer paradigm
• Media-aware transport ? superior system
  performance

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The End

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