Transport of
Real-Time Traffic
over the Internet
Bernd Girod
Information Systems Laboratory
Stanford University
THE MEANING OF FREE SPEECH
The acquisition by eBay of Skype is a
helpful reminder to the world's trilliondollar 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 . . .
[Economist, September 2005]
B. Girod: Internet Real-Time Transport, September 2005
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IPTV Rollout
Verizon
10M households
by 2009
IPTV SBC
18M households
by 2007
[IEEE Spectrum, Jan. 2005]
B. Girod: Internet Real-Time Transport, September 2005
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Why Is Real-Time Transport Hard?
Internet is a best-effort network . . .
Congestion
Packet loss
Delay
Insufficient rate to communicate
Impairs perceptual quality
Impairs interactivity of services;
Telephony: one way delay < 150 ms
[ITU-T Rec. G.114]
Delay jitter
Obstructs continuous media playout
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Outline of the Talk
•
•
•
•
•
QoS vs. best effort
Resource allocation for IPTV
Rate-distortion optimized streaming
Multi-path routing
P2P multicasting of live video streams
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How 1B Users Share the Internet
TCP Throughput
Rate r
maximum
transfer
unit
data rate
Growing
congestion
1.22  MTU
r
RTT  p
round
trip time
packet
loss rate
0.0001
0.001
0.01
0.1
p
[Mahdavi, Floyd, 1997]
[Floyd, Handley, Padhye, Widmer, 2000]
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QoS vs. Best Effort
Reservation-ism
Best Effort-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
B. Girod: Internet Real-Time Transport, September 2005
– 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
7
Simple Model of A Shared Link
• Link of capacity C is shared among k flows
C
• Fair sharing: each flow uses data rate C/k
• Homogeneous flows with same utility function u(.)
• Total utility
C 
U k   k u  
k
[Breslau, Shenker, 1998]
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Rigid Applications
• Utility u=0 below of
minimum bit-rate B
u
1
B
• Maximum total utility U=k* is achieved by
admitting at most k* flows
C/k

 C  C 
k *  arg max  k  u      
k
 k   B 

[Breslau, Shenker, 1998]
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Rigid Applications (cont.)
• Expected loss in total utility w/o admission control

C  C 
DU  Pr k      
 B   B 

• 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
u(C/k)=1-aC/k
C/k
• All flows should be admitted: best effort!
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Video Compression
• H.264 video coding for 2
different testsequences
Good
picture
quality
44
42
Y-PSNR in dB
40
38
36
34
32
Foreman
mobile
30
Bad
picture
quality
• 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
Mobile
foreman
28
26
24
0
500
1000 1500 2000 2500 3000 3500 4000
encoding rate in kbps
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Different Utility Functions
• Example: uk(rk)=1-akrk
Equal-slope
“Pareto condition”
uk
rk
Vilfredo Pareto
1848-1923
• With rk>=0  Karush-Kuhn-Tucker conditions
(“reverse water-filling”)
• Better than utility-oblivious “fair” sharing
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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
0
Decoder
0
Transcoder
1
Decoder
1
Transcoder
2
Decoder
2
Transcoder
3
Decoder
3
Receiver
(Multi-Channel)
Controller
[Kalman, van Beek, Girod 2005]
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Tx Backlog for 4 Video Streams
backlog in frames
85% WLAN Utilization
10
10
8
8
6
6
4
4
2
2
backlog in frames
0
0
10
20
30
40
0
10
10
8
8
6
6
4
4
2
2
0
0
10
20
30
40
time in seconds
0
0
10
20
30
40
0
10
20
30
40
time in seconds
[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 1000s
simultaneous
streams
DSL
Cable
wireless
Server
Network
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Client
17
Not All Packets are Equally Important
I
I
I
B
P
B
P
B
P
I
B
P
B
P
B
P
…
…
…
A
A
B. Girod: Internet Real-Time Transport, September 2005
…
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Not All Packets are Equally Important
I
I
I
B
P
B
P
B
P
I
B
P
B
P
B
P
…
…
…
A
A
B. Girod: Internet Real-Time Transport, September 2005
…
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Distortion-Aware Packet Dropping
Good
Picture
quality
Bad
picture
quality
Distortion
aware
Oblivious
Packet dropping
No retransmissions
QCIF Carphone
I-P-P-P-P-P- . . .
Percentage of Packets Retained [%]
[Chakareski, Girod, ICME 2004]
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Rate-Distortion
Optimized (RaDiO) Streaming
“Decide which packets to send (and when) to maximize
picture quality while not exceeding an average rate” [2001]
Repeat
request
Video
data
Rate-distortion
preamble
Request
Packet
stream
schedule
Server
Network
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Client
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A 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]
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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
Congestion D
[seconds]
Example: M/M/1 model
D=
1
R max -R
Rmax
Rate R
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Video Distortion with Self Congestion
Good
Picture
quality
Self congestion
causes late loss
Bad
picture
quality
Bit-Rate [kbps]
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Streaming with Last Hop Bottleneck
Random cross traffic
High bandwidth links
Video traffic
Acknowledgments
B. Girod: Internet Real-Time Transport, September 2005
Low bandwidth
last hop
25
Delay distribution
• Overall delay distribution
pdf
delay
• Queue length determines delay of last hop
C
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PSNR [dB]
PSNR [dB]
Comparison RaDiO vs. CoDiO
Rate [kbps]
Simulations using H.263+
50 %
End-to-end delay [ms]
Rate : 10 fps
Sequence : Foreman (32kbps,32kbps)
Sequence length : 60s
Playout deadline : 600ms
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How To Avoid Traffic Jams?
• Avoid congested times . . .
Congestion-aware packet
scheduling
• Avoid congested roads . . .
Congestion-aware routing
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Multipath Routing for Minimum Congestion
Mesh network, fully connected
 Streaming 100 kbps from Node 1 to Node 5
 Random cross traffic

31 kbps
16kbps
15
77
5
2
18
23
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2
45
7
35
8
23
24
8
43
6
64
24
249
29
Multipath Video Streaming
Good
Picture
quality
6 dB
Sequence : Foreman QCIF,
250 frames, 30 fps
Codec: H.26L TML 8.5
Bad
picture
quality
Playout deadline : 500 ms
Packetization : 1 frame/packet
Traffic model: CBR
No. of realizations: 400
Bit-Rate [kbps]
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Multipath Video Streaming
1 path
80 kbps, PSNR 32.5 dB
B. Girod: Internet Real-Time Transport, September 2005
3 paths
187 kbps, PSNR 36.2 dB
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Distribution of Live Streams
via “Pseudo-Multicast”
Content delivery network
Example
Media
server
AOL webcast of Live 8 concert
July 2, 2005
...
Splitter
servers
1500 servers in 90 locations
50 Gbps
... ... ... ...
...
175,000
simultaneous
viewers
8M unique viewers
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Distribution of Live Streams
via “Pseudo-Multicast”
Content delivery network
Splitter
servers
Example
Media
server
AOL webcast of Live 8 concert
July 2, 2005
...
300 kbps
P2P live multicast
... ... ... ...
1500 servers in 90 locations
50 Gbps
...
175,000
simultaneous
viewers
8M unique viewers
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P2P Multicast over 1 Tree
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P2P Multicast over 2 Trees
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P2P Ungraceful Parent Leave
New
parent
Parent
leave
Retransmissions
Parent
yellow
Yellow
tree isis
3 of
trees
selected
detected
tree requested
is down
recovered
Hello, Yellow
Tree Parent?
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Experimental 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]
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Join and Rejoin Latencies
[Setton, Noh, Girod, ACM MM 2005]
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Congestion-Distortion
Optimized P2P Live Streaming
Without CoDiO
% peers
connected
to 4/4 trees
With CoDiO
% peers
connected
to 4/4 trees
[Setton, Noh, Girod, ACM MM 2005]
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P2P Video Multicast: 64 out of 300 Peers
Congestion-distortion optimized
(CoDiO) streaming
H.264 @ 220 kbps
2 sec latency for all streams
B. Girod: Internet Real-Time Transport, September 2005
Without CoDiO
40
Concluding 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
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The End
http://www.stanford.edu/~bgirod/publications.html