Advanced Techniques for Continuous Multimedia Stream Delivery

1. Proxy-Assisted Techniques for Delivering Continuous Multimedia Streams Lixin Gao, Zhi-Li Zhang, and Don Towsley

2. Agenda • Related work • Proxy-Assisted Video Delivery Architecture • Proxy-Assisted Catching • Proxy-Assisted Selective Catching • Simulation results • Conclusion

3. Related Work • Server-push -> Typically designed for “hot” (frequently requested) objects -> Fixed number of multicast channels

4. Limitations of current technology • Server and network resources (Server I/O bandwidth and network bandwidth) are major limiting factors in widespread usage of video streaming over the internet • Need techniques to efficiently utilize server and network resources • Service latency and popularity of video object should be considered

5. Proxy-Assisted Video Delivery Architecture

6. Advantages of proxy-assisted video delivery • Latency reduction without increasing demand on backbone network resources • Need to store only the initial frames hence feasible with large data volume • I/O bandwidth requirement on proxy server is insignificant, since responsible for limited number of clients

7. Classification • Proxy-assisted catching : Suited for “hot” video objects • Proxy-assisted selective catching : Even suited for “cold” (less frequently requested) video objects

8. Advantages of proposed architectures • Reduce the resources requirements at central server • Reduce service latency experienced by clients Assumptions • Client can receive data from 2 channels simultaneously

9. Proxy-Assisted Catching • Reduces service latency by allowing clients to join an ongoing broadcast • Clients catch-up by retrieving initial frames using unicast channel from proxy

10. Proxy-Assisted Catching Partition function used

11. Optimizing • Server and network bandwidth are major bottleneck. Hence reducing total number of channels required • Trade-off between -> Number of dedicated channels by server -> Storage space required by proxy

12. Terms involved • N : No. of video objects on central server • L : Length of video • λ : Request rate (Poisson distribution) • K : Server channels to broadcast video • K* : Optimal number of server channels • i : Video object no. • j : Broadcasting frame

13. Calculation • No. of proxy channels required : • Total no. of channels required : • Tradeoff between number of server channels and expected number of proxy channels required for catch-up

14. Calculation contd.. • Optimization problem : • Expected number of channels : Optimal no. of server channels Optimal no. of proxy channels

15. Controlled Multicast • Client pull technique • Allows client to join the ongoing multicast if it requests with a certain threshold time Ti • Else a new multicast channel is allocated Proxy-assisted Controlled Multicast • Proxy pre-store the initial Ti frames of video • Missing portion of video is send separately through a unicast channel • Good technique for “cold” video objects

16. Comparison with Proxy-Assisted Controlled Multicast • Total no. of channels required for controlled multicast is : • For large value of λ no. of channels required by proxy-assisted catching is less • Verified using following setup : L : 90 min. video object

17. Observation 0.4

18. Proxy-Assisted Selective Catching • Combines Proxy-Assisted Catching and Controlled Multicast • Broadcast most frequent videos using Proxy-Assisted Catching and less frequent videos using Controlled Multicast

19. Classifying “Hot” and “Cold” videos • Hot video if Total no. of channels required using catching Total no. of channels required using controlled multicast

20. Simulation results • Simulation settings N : No. of video objects on central server λ : Request rate (Poisson's distribution) • Simulates 150 hours of client requests • Ki* : Broadcasting channels for “hot” video objects • Remaining channels for controlled multicast • First-come-first-serve basis

21. Assumptions • Sufficient proxy resources to store prefixes for all videos • Proxy server has 40GB of storage space and I/O bandwidth of 88 Mb/s

22. Waiting time vs. total number of channels λ = 50 710 900

23. Waiting time vs. Arrival rate • λ varies from 40 to 80 • Total no. of channels = 700

24. Total no. of channels vs. arrival rate 100 150 Performance of selective catching and catching same

25. Waiting time vs. Server channels 700 460 • 36% saving in number of channels required at central server

26. Number of channels vs. Arrival rate • Significant reduction in central server channel requirement

27. Waiting time vs. Server channels • Advantage of proxy-assisted selective catching does not critically depend on availability of proxy storage space

28. Conclusion • Approach is proved using quite realistic simulations without any major assumptions • If the arrival rate exceeds beyond certain assumptions then the service latency will increase