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Broadcasting Delay-Constrained Traffic over Unreliable Wireless Links with Network Coding

Broadcasting Delay-Constrained Traffic over Unreliable Wireless Links with Network Coding. I-Hong Hou and P.R. Kumar. Wireless Broadcasting: Video Streaming. Application Characteristics. No per-packet delay bounds Need to delivery every packet correctly. Traditional Applications.

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Broadcasting Delay-Constrained Traffic over Unreliable Wireless Links with Network Coding

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  1. Broadcasting Delay-Constrained Traffic over Unreliable Wireless Links with Network Coding I-Hong Hou and P.R. Kumar

  2. Wireless Broadcasting: Video Streaming

  3. Application Characteristics • No per-packet delay bounds • Need to delivery every packet correctly Traditional Applications Video Streaming • Strict per-packet delay bounds • Expired packets are not useful • Can tolerate a small amount of packet losses

  4. Performance in the Future High Throughput≠

  5. Performance in the Future High Timely Throughput= Timely Throughput: Throughput of packets that are delivered on time

  6. Challenges from Wireless Transmissions • Wireless transmissions are subject to shadowing, fading, and interference • Therefore, wireless transmissions are unreliable

  7. Challenges from Wireless Broadcast • ACKs are not implemented in broadcast • Costly to obtain feedbacks from all clients • No per-transmission feedback information

  8. Challenges from Wireless Broadcast • ACKs are not implemented in broadcast • Costly to obtain feedbacks from all clients • No per-transmission feedback information

  9. System Model for Wireless Broadcast with Delay Constraints

  10. Client-Server Model Timeline Clients Flows 1 A B C AP 2 3

  11. Traffic Model 1 A B C Packet Generation AP 2 Interval 3

  12. Traffic Model 1 A A B B B C C C AP 2 A B C C B 3

  13. Model for Delay Constraints 1 A B C Packet Generation AP 2 Deadline Interval 3

  14. Model for Delay Constraints Delays of delivered packets are no larger than the length of an interval 1 A B C A,C expire AP 2 A C Interval 3

  15. Model for Unreliable Broadcast Client n receives each transmission successfully with prob. pn 1 A B C p1 p2 AP 2 A B p3 C C B 3

  16. Scheduling Example 1 A B C p1 A A A A A p2 AP 2 X A B p3 C C B A 3 X

  17. Scheduling Example Duplicate Packets are ignored 1 X A B C p1 A A A A A A p2 AP 2 X X A B p3 C C B A A 3 X A

  18. Scheduling Example 1 X A B C p1 A A X B X C C C C C p2 AP 2 X C B X A B X C p3 C C B B C C A A C 3 X X X X A C

  19. Timely Throughput 1 X p1 A A X B X C p2 AP 2 X C B X X C p3 3 X X X X A C

  20. Timely Throughput 1 X p1 A A X B X C p2 AP 2 X C B X X C p3 3 X X X X A C

  21. Timely Throughput Requirements A C B C B 1 p1 X A A X X B C p2 A C B C B AP 2 X X C C B X p3 A C B C B X A C 3 X X X

  22. Summary of Model • Flows have strict per-packet delay bound • Clients have timely throughput requirements on each flow • Wireless transmissions are unreliable • AP does not have feedback information Goal: • Design policies to fulfill timely throughput requirements for all flows and all clients as long as they are feasible

  23. Scheduling Policies

  24. Delivery Debt Slope = qA,1 Delivery Debt

  25. Expected Delivery Debt • AP does not have feedback information • But, AP can estimate packet deliveries • Expected delivery debt for client n and flow i at the kth interval di,n(k):= kqi,n-E{# of packets client n receives from flow i} Client n receives A with probability 1-(1-pn)2, and receives B with probability pn AP A A B

  26. A Framework for Designing Policies • Policy: Maximize ∑di,n(k)+Prob(client n receives a packet from flow i) in every interval • Theorem: This policy fulfills a system as long as it is feasible • Feasibility Optimal Policy

  27. A Policy without Coding • Marginal Delivery Probability (mi,n): prob. that client n receives a new packet from flow i in a particular transmission • Greedy Algorithm: schedule the flow i that maximizes ∑ndi,n(k)+mi,n in every time slot mA,n =pn mA,n =pn(1-pn) mA,n =pn(1-pn)2 AP A A A

  28. Optimality Result • Greedy Algorithm is feasibility optimal • Polynomial complexity per interval • However, it is only optimal among policies that do not employ network coding Can we improve performance by employing network coding?

  29. Network Coding: XOR Coding Client cannot obtain packet A Duplicate Packet B X X X B X 1 B B A A B A AP

  30. Network Coding: XOR Coding • XOR Coding: AP can broadcast packets contain A, B, or Client obtains both packets X X X B X 1 A A B B AP

  31. Pairwise XOR Policy • Design of Pairwise XOR Policy: • Only allow pairwise XOR • Satisfy some mild restrictions derived from Greedy Algorithm • Theorem: Pairwise XOR Policy is feasibility optimal among all policies that satisfy the mild restrictions. Pairwise XOR Policy fulfills every system that can be fulfilled without coding • Polynomial complexity per interval

  32. Network Coding: Linear Coding Client cannot obtain packet A Duplicate Packet B X X X B X 1 B B A A B A AP

  33. Network Coding: Linear Coding • Linear Coding: AP broadcasts linear combinations of packets from flows Client obtains both packets A+4B A+5B X X X X 1 A+B A+2B A+3B A+4B A+5B A+6B AP

  34. Optimal Grouping Policy • Design of Optimal Grouping Policy: • AP broadcasts linear combinations of packets • Satisfy some mild restrictions derived from Greedy Policy • Theorem: Optimal Grouping Policy is feasibility optimal among all policies that satisfy the mild restrictions. Optimal Grouping Policy fulfills every system that can be fulfilled without coding • Polynomial complexity per interval

  35. Simulation Results

  36. VoIP Traffic • ITU-T G.711 • Packet size = 160 Bytes • Interval length = 40 ms • IEEE 802.11b • Transmission rate = 11 Mb/s • 20 time slots in an interval

  37. Network Topology • 20 clients and one AP • AP broadcasts 10 flows • qi,n= α, for 1 ≤ i ≤ 5; qi,n= β, for 6 ≤ i ≤ 10

  38. Simulation Result Plot all (α, β) that can be fulfilled by each policy

  39. Conclusion • Studied the problem of broadcasting delay-constrained flows through wireless links • Proposed a model that jointly considers the following: • Per-packet delay bounds of flows • Timely throughput requirements of clients for each flow • Unreliable wireless transmissions • Lack of per-transmission feedbacks in broadcast • Proposed a policy that is feasibility optimal • Explored the usage of network coding to enhance performance

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