1. Adaptive Transmission for layered streaming in heterogeneous Peer-to-Peer networks Xin Xiao, Yuanchun Shi, Yuan Gao Dept. of CS&T, Tsinghua University Beijing, China 2008.10.13

2. Background heterogeneous networks how to transmit video stream? “One-fits-all”: no longer suited

3. why layered (scalable) coding? multi-version v.s. layered coding high-quality video multi-version video low-quality video enable dynamic video quality adaptation! two-layer video adaptive-layer video layered-coding

4. how layered coding? mechanism of layered coding base layer enhancement layers

5. what is our work? in an heterogeneous peer-to-peer network to adaptively transmit layered streaming to a large number of users the goal is divided into two parts high performance overlay construction i.e. the newly joined node how to select neighbors optimal data scheduling i.e. how to request and relay data

6. Toward High Performance Overlay Construction for Layered Streaming

7. what’s characteristics of overlay construction for layered streaming? 1 、 connection condition is not the unique criterion for QoS 2 、 greedy neighbor selection is apt to construct poorer overlay D

8. Overlay Construction Phase Key idea network condition and providing layers as a whole rather than selecting neighbors just according to their network conditions should probe and find appropriate logical partners for each layer avoidance of greedy neighbor selection guarantee the QoS of new node as well as improve the QoS of existing nodes

9. Overlay Construction Phase Step 1: Probing existing nodes Step 2: QoS-aware neighbor selection 1 、 —— improve j’s QoS 2 、 RTTji is very small among all the RTTs of node i and the replying nodes —— guarantee i’s QoS poor node replacement in setp2

10. Neighbor selection algorithm ½ neighbors are selected to improve existing nodes’ QoS and the other ½ are selected to guarantee the new node’s QoS

11. Toward Optimal Data Scheduling for Layered Streaming

12. Four goals for data scheduling Unlike non-layered streaming where the optimization objective is almost equal to maximizing the throughput and/or minimizing the packet delay Goals for layered streaming Throughput and Delay Layer Delivery Ratio Useless Packet Ratio Jitter Prevention

13. 3-Stage Model for Scheduling Node’s buffer is divided into 3 stages Free Stage free request data, to guarantee throughput Decision Stage decide on subscribing layers, to ensure delivery ratio, useless packets ratio. Jitter prevention is also considered Remedy Stage re-fetch the missed minor blocks within the subscribed layers

14. 3-Stage Model for Scheduling Remedy Stage k-window remedy mechanism assume n blocks missed when the window just entered remedy stage thus n/k blocks should be requested within each remedy window especially important when the node encounters bandwidth burst-and-drop in the decision window

15. 3-Stage Model for Scheduling Decision Stage probability decision mechanism assume when the window just enters decision stage the missed blocks number is m l in layer l the neighbors can supply s l (under bandwidth constraint) thus delivery ratio of layer l is: DR l = 1 – (m l - s l ) / wnd_blocks l subscription probability is:

16. 3-Stage Model for Scheduling Decision Stage (cont.) to prevent jitter, define Jitter-Prevent-Factor (or JPF) for each layer l: Improved Delivery Ratio, or IDR is: IDR l = DR l * JPF l Re-calculate SP(l) with IDR l

17. 3-Stage Model for Scheduling Free Stage Scheduling with Min-Cost Flow Model, to ensure high throughput Importance Definition of the blocks related to playback time, layer, the number of neighbors that own it

18. Implementation In NS-2 simulator test our approach in comparison with others the underlying link-layer topology is generated by GT-ITM On Internet we have been developing real system with support of ACE (Adaptive Communication Environment) SDK—C++, platform independent with PFGS layered encoding

19. Evaluation Experiment results for Overlay Construction on throughput, delay, in comparison with SCAMP ： based on Gossip ， random neighbor selection Narada ： based on QoS selection

20. Evaluation joining time recovery timebenefit of node replacement Experiment results for Overlay Construction

21. Evaluation Experiment results for Data Scheduling on the above four goals, with PALS, Chainsaw and Pure-MCFP

22. Evaluation Experiment results for Data Scheduling on the impact of remedy window number k, and ratio std

23. Implementation In Real Network The PFGS-based layered streaming Recovered with base layerRecovered with base & enhancement layers

24. Implementation In Real Network Deployment in the PDEPS (Project of Digital Education for Public Service) project to deliver live teaching broadcast to users with heterogeneous networks the first practical layered streaming system for education in peer-to-peer network run on PC run on PDA