1 / 39

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.

amma
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related