Layer-Encoded Video in Scalable Adaptive Streaming

1. Layer-Encoded Video in Scalable Adaptive Streaming Michael Zink, Jens Schmitt, and Ralf Steinmetz, Fellow, IEEE IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 7, FEBRUARY 2005

2. Outline • What is scalable adaptive streaming? • Maximize the quality of the cached video • Average PSNR is not a good assessment • Subjective impression of variations in layer-encoded video • Retransmission scheduling • Simulation results • Conclusion

3. What is scalable adaptive streaming • Main goal: • Provide a true video-on-demand (TVoD) system • Issues: • Quality Adaptation (congestion control) • Layer-encoded technique • Scalability (performance) • Proxy caching

4. How to assess the quality of video • Use a subjective assessment • The experiment was performed with 94 test candidates • (62 males and 32 females), between the age of 14 and 64. • Video: • Consists of 4 layers • The size of the nth layer: sn = 2sn-1

5. Subjective assessment v.s PSNR (1/2) • Six variations in layer-encoded video • Farm1–Amplitude: • Decrease slightly has better quality • Farm2–Frequency: • Lesser layer changes has higher quality

6. Subjective assessment v.s PSNR (1/2) • M&C1–Closing the Gap: • Close a gap in a lower layer has higher quality • M&C2–Constancy: • No layer changes has better quality

7. Subjective assessment v.s PSNR (2/2) • M&C3–Constancy at a Higher Level: • Tennis1–All is Well That Ends Well: • Increasing the amount of layers in the end leads to a higher quality

8. Compare Subject and PSNR quality • Subject assessment is more precise than PSNR. • s(v) represents the same result as subject assessment. • Minimize the frequency and amplitude will have the better quality (reducing the layer variations).

9. When to schedule the retransmissions • Retransmission time: • Directly after the initial streaming process • During subsequent requests • During requests for different content from the server

10. layers frames How to schedule the retransmissions • Scheduling goals: • Decreasing quality variations for a cached video is important. • Minimize both frequency of variations and amplitude of variations. s(v): spectrum

11. Algorithms for retransmission scheduling (1/3) • Optimal retransmission scheduling • is a discrete nonlinear stochastic optimization problem (NP-complete) • Heuristics for retransmission scheduling • Unrestricted priority-based heuristics • Send periodic retransmission requests to the server to ensure that the server obtains an up-to-date schedule of retransmissions.

12. Algorithms for retransmission scheduling (2/3) • Three heuristics of unrestricted priority-based retransmission scheduling algorithm: • Case1 : Unrestricted lowest layer first (U-LLF)

13. Algorithms for retransmission scheduling (3/3) • Case2 : Unrestricted shortest gap lowest layer first (U-SG-LLF) • Case3 : Unrestricted lowest layer shortest gap first (U-LL-SGF)

14. Simulation results (1/2)

15. Simulation results (2/2)

16. Conclusion • Promise a scalable TCP-friendly TVoD system • Scalable adaptive system • Use both caching and layer-encoded • Develop the retransmission algorithms to retransmit missing segments • Minimize the frequency and amplitude of the variations of the cached video