Multipath Routing of Multimedia Data Over Ad Hoc Wireless Networks

1
Multipath Routing of Multimedia Data Over Ad Hoc
Wireless Networks
2
Outline

• Ad hoc wireless networks
• Routing for ad hoc wireless networks
• Source routing and multi-path routing
• Example with Dynamic Source Routing (DSR)
• Congestion optimized stream routing
• Complete/practical solution
• Comparison with a heuristic scheme
• Video distortion model
• Experimental results
• Network congestion
• Two applications data download video streaming
• Conclusion

3
Wireless Ad Hoc Networks

• Collection of wireless nodes with no
  infrastructure
• Every node can be source, destination or relay
• Many applications search and rescue, disaster
  areas

4
Ad Hoc Wireless Network Model
15-node network
If nodes are not limited in their transmissions,
we get the following formulas

• Assumptions
• Static nodes
• Bandwidth 2.2 MHz
• Interference limited network
• Every node has global information

5
Existing Routing Algorithms

• Routing for wireless ad hoc networks
• DSDV
• AODV
• DSR
• TORA
• Optimization and routing
• Flow assignment
• Resource allocation

Perkins and Bhagwat, 1994
Perkins and Royer, 1999
Johnson and Maltz, 1996
Park and Corson, 1997
Kleinrock, 1976, Bertsekas Gallager, 1987
Xiao, Johansson and Boyd, 2002
6
DSR example

• DSR allows to discover and maintain routes on ad
  hoc wireless networks

• Example route from node 1 to node 5
• If no route is cached the route discovery
  protocol is initiated
• route request broadcast
• intermediate nodes append their address and
  re-broadcast the reply
• reply is sent back by the first node which knows
  how to reach the destination

7
Congestion Optimized Stream Routing

• Congestion may be estimated by the average
  queuing delay for a packet on the network
• Average delay over a link for the M/M/1 model
• So minimizing the congestion results in the
  following problem

i
Cross traffic Fij
Optimal fij ?
j

• Subject to
• rate constraints at the source and destination
• flow conservation, flow positivity
• capacity constraints Fij fij lt Cij

8
Illustration
Source
i
Cross traffic Fij
Optimal fij ?
j
Destination
9
Solution Example

• Streaming 100 kbps from node 1 to node 5

10
Solution Example

• Properties of the solution
• Diversity of paths
• Complex
• Linear convergence
• To lower the complexity
• Predetermine a set of paths
• Optimal flow partition among the paths
• Instantaneous convergence

Example over 3 paths 1 2 10 4 5 1 3 7 6 5
1 2 9 8 15 5
11
Which Set of Paths ?

• First method
• Solve the initial problem
• Extract the routes carrying the most traffic
  recursively
• Partition over the k best
• Practical method based on DSR
• Discover multiple routes through route
  request/reply broadcasts
• Add the link state to the nodes address
  information
• Partition over these routes

12
Comparison to Load Balancing

• Heuristic scheme for traffic partitions
• Load balancing among bottleneck links on each
  path
• Oblivious to joint links, length of path etc

• Solution examples

3-path routing
6-path routing
13
Video Distortion Model Encoder

• Encoder distortion
• MSE measure for distortion
• D0, ? and R0 are estimated via regression

Stuhlmuller 2000
14
Video Distortion Model Packet Loss

• Transmission distortion

• Prand is random packet loss rate
• M/M/1 model for delay
• ? is related to coding structure
• C is the maximum rate supported by the routes
• Ttarget is determined from empirical data

15
Video Distortion Model Combined

• Total distortion

Encoder MSE
Trans. MSE

• Decode video quality is limited by encoder
  performance at low rate, and network congestion
  at high rates

16
Network Simulation Setup

• Two application scenarios
• Data download
• Live video streaming
• NS-2 configuration
• 15 static nodes, routing from node 1 to node 5
• No random packet loss, no propagation delay
• UDP connection, source routing
• M/M/1 model for data and cross traffic
• Constant Bit Rate (CBR) traffic for video
• http//www.isi.edu/nsnam/ns/

17
Data Download Results Congestion
Traffic model M/M/1 No. of samples 600,000
18
Video Streaming Results Congestion
Sequence Foreman QCIF Sequence length 250
f. Codec H.26L TML 8.5 Frame rate 30
fps Playout deadline 500 ms Packetization 1
f./packet Traffic model CBR No. of
realizations 400 No random loss
19
Video Streaming Results End to End Delay
average end to end delay
90th percentile end to end delay
Traffic model CBR No. of realizations 400
20
Video Streaming Results RD Performance
Sequence Foreman QCIF Sequence length 250
f. Codec H.26L TML 8.5 Frame rate 30
fps Playout deadline 500 ms Packetization 1
f./packet Traffic model CBR Number of
realizations 400 Packet loss rate 0
21
Video Streaming Results Sequence
Comparison of the operating point for different
number of paths
Foreman QCIF Sequence
1 path 80 kbps, PSNR 32.5 dB
3 paths 187 kbps, PSNR 36.2 dB
6 paths 278 kbps, PSNR 38 dB
22
Video Streaming Results Sequence
Comparison of the operating point for the
heuristic and proposed schemes
6 paths heuristic 187 kbps, PSNR 36.2 dB
6 paths optimal 278 kbps, PSNR 38 dB
23
Video Streaming Results GOP length
Sequence Foreman QCIF Sequence length 250
f. Codec H.26L TML 8.5 Frame rate 30
fps Playout deadline 350 ms Packetization 1
f./packet Traffic model CBR Number of
realizations 400 Packet loss rate 1
24
Conclusions

• Congestion optimized stream routing
• Convex optimization formulation
• Efficient utilization of limited bandwidth
  resource
• Outperforms heuristic load balancing
• Video distortion model
• Combines the influence of encoder distortion with
  packet delay
• Predict and compare behaviors of different coding
  schemes
• Network simulation
• Applied to data download and live video streaming
• Demonstrated the advantage of the proposed
  routing scheme
• Verified the video distortion model