1. Ying Wai Wong, Jack Y. B. Lee, Victor O. K. Li, and Gary S. H. Chan CSVT 2007 FEB Supporting Interactive Video-on-Demand With Adaptive Multicast Streaming

2. Introduction Multicast Streaming Interactive Playback Support Interactive Multicast Streaming Static Full Stream Scheduling (SFSS) Adaptive Full Stream Scheduling Performance Evaluation

3. Multicast Streaming Concept 10:00 10:03 10:05 Tcsu (http://vc.cs.nthu.edu.tw/home/paper/codfiles/tcsu/200104221412/odmr_jswang.ppt) time 10:0010:0310:05 Stream 1Stream 2Stream 3

4. Multicast Streaming Caching and Patching Caching and Sharing the streams in clients The missed initial portion is transmitted by patching Full Stream Patching Caching in c b Caching in c c Caching in c b Caching in c c time Partial Stream

5. Multicast Streaming Controlled greedy recursive patching (CGRA) [9] A new client caches data from the latest reachable stream Cost-aware recursive patching (CARP) [9] A new client is inserted in the merge tree Dyadic [13] Dyadic interval: t + L/(2r i ) Earliest Reachable Merge Target (ERMT) [10] time Time to wait before playback beginning

6. Interactive Playback Support Unicast Dedicated stream Multicast Discontinuous interactive playback Staggered multicast video streaming [30] Split-and-Merge (Dedicated interactive stream) [28] Best-Effort Patching (cache from more than one stream) [33] 678 … … current Jump point stream pkpk tmtm time Current point Stream 1 Stream 2 Stream 3 Stream 4 Dedicated stream p k -t m

7. Interactive Multicast Streaming Issues Interactivity Model Request Scheduling Client Buffer Management

8. Interactive Multicast Streaming Interactivity Model Interactive operations (VCR operations) Pause/resume, slow motion, frame stepping, fast forward/backward visual search, and forward/backward seeking Multi-campus interactive educational resource system [37] Exponential distribution Not suitable for entertainment contents Two state model – NORMAL and INTERACTION [36] Exponentially distributed staying in one state Multi-state model [31]

9. Interactive Multicast Streaming Request Scheduling Admission requests Generated by new clients Merging requests Generated by clients performing VCR Importance Merging requests > Admission requests Full-stream restart threshold W WW time Partial stream (Merging requests) time Full stream (admission requests)

10. Interactive Multicast Streaming Client Buffer Management Playback rate: R bps Receiving rate: 2R bps Buffer accumulation rate: R bps Minimum required buffer size: WR bits Assume maximum buffer size is limited to B c R bits W  B c (p k – t m )  B c T p + T pc  B c PAUSE duration Patching and caching duration W time Caching Maximum buffer time Nearest playback point after VCR operations VCR duration

11. Interactive Multicast Streaming Performance Impact Time to wait before playback beginning Time to wait from VCR to playback resuming # of server channels: 10 Probability of FSEEK:0.1

12. Interactive Multicast Streaming Performance Impact (2) # of server channels: 24 Probability of FSEEK:0.03 Probability of BSEEK:0.03 Probability of PAUSE:0.03

13. Static Full Stream Scheduling (SFSS) Assumption: Full streams are generated every W seconds Merging request rate: u VCR requests/second # of full streams in the system: L/W Average playback point after VCR operations before next full stream: W/2 Average merging cost: WR/2 Average system cost: u VCR (WR/2) Resource consumption rate of full stream: RL/W Total resource consumption rate: u VCR (WR/2) + RL/W When W = (2L/u VCR ) 1/2, we have the minimum consumption rate WW time … W is a decreasing function of u VCR Partial stream FSEEK,BSEEK,PAUSE

14. Adaptive Full Stream Scheduling An optimal W must be found with knowing all the system parameters. Five system parameters Client arrival rate, Probability of FSEEK P f, Probability of BSEEK P b, Probability of PAUSE P p, Average seek distance  sd 1. Initial full-stream restart threshold (W) is needed such that system parameters can be measured. 2. Embedded simulator is applied to find the optimal threshold by the measured parameters. 3. The changes of the system parameters are detected and go to step 2. 95% confidence interval

15. Performance Evaluation Optimization of the Full Stream Restart Threshold # of server channels: 24 Probability of FSEEK:0.1 Probability of BSEEK:0.1 Probability of PAUSE:0.1

16. Performance Evaluation Latencies Comparisons Improvement: 98%

17. Performance Evaluation Latencies Comparisons Improvement: 90% # of server channels: 24 Probability of FSEEK:0.05 Probability of BSEEK:0.05 Probability of PAUSE:0.05

18. Performance Evaluation Effect of Client Buffer Constraint L

19. Performance Evaluation Just-in-Time Simulation (Adaptive W)

20. Performance Evaluation Just-in-Time Simulation (Adaptive W)

21. Performance Evaluation Adaptive FSS vs. Static FSS