1 Scheduling for Variable-Bit- Rate Video Streaming By H. L. Lai

2 Contents Variable-Bit-Rate Videos Bit-Rate Smoothing Monotonic Decreasing Rate Scheduling Aggregated Monotonic Decreasing Rate Scheduling Conclusions Q&A

3 Variable-Bit-Rate Videos CBR vs. VBR Problems with VBR

4 CBR vs. VBR 2 types of video compression: CBR compression Constant bit-rate Variable visual quality VBR compression Variable bit-rate Constant visual quality

5 Problems with VBR Complex admission control and scheduling Hard to provide performance guarantee Solution: Smoothing

6 Bit-Rate-Smoothing Principle Design Considerations Review of smoothing algorithms

7 Principle

8 Design Considerations Lossless or lossy video? Stored video or live video? Zero or non zero playback delay? Deterministic or statistical performance guarantee?

9 Optimal Smoothing Algorithm J. D. Salehi, S.-L. Zhang, J. Kurose, and D. Towsley, “ Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing ”, IEEE/ACM Transactions on Networking, pp , vol. 6, issue 4, Aug Minimal variability Minimal peak rate

10 Piecewise Constant Rate Transmission and Transport J. McManus and K. Ross, “ Video on demand over ATM: constant-rate transmission and transport ”, Proceedings of IEEE INFOCOM, pp , Mar Control the separation and no. of bit-rate changes

11 CBA & MCBA Critical Bandwidth Allocation (CBA) W. Feng and S. Sechrest, “ Critical bandwidth allocation for the delivery of compressed video ”, Computer Communications, pp , vol. 18, no. 10, Oct Minimum changes Bandwidth Allocation (MCBA) W. Feng, F. Jahanian and S. Sechrest, “ Optimal buffering for the delivery of compressed prerecorded video ”, ACM Multimedia Systems Journal, Sep Minimal peak rate Minimal BW increases (CBA) Minimal BW changes (MCBA)

12 Rate Constrained Bandwidth Allocation W. Feng, “ Rate-constrained bandwidth smoothing for the delivery of stored video ”, SPIE Multimedia Networking and Computing, pp , Feb Check all frame sizes Prefetch earlier if any frame exists BW constraint

13 Time Constrained Bandwidth Allocation W. Feng, “ Time constrained bandwidth smoothing for interactive video-on-demand systems ”, International Conference on Computer Communications, pp , Nov Construct an upper bound curve with both buffer and time constraints Construct schedule with any other smoothing algorithms

14 ON-OFF Scheduling R.-I Chang, M. C. Chen, J.-M. Ho and M.-T. Ko, “ Designing the ON-OFF CBR transmission schedule for jitter-free VBR media playback in real-time networks ”, Proceedings of the Fourth International Workshop on Real-Time Computing Systems and Applications, pp. 2-9, Oct Single rate for whole system Send “ as late as possible ”

15 Other Studies Smoothing at multiple intermediate nodes J. Zhang, “ Using multiple buffers for smooth VBR video transmissions over the network ”, 1998 International Conference on Communication Technology, pp , vol. 1, Oct Multiplexing optimally smoothed schedules W. Zhao and S. K. Tripathi, “ Bandwidth-efficient continuous media streaming through optimal multiplexing ”, Proceedings of International Conference on Measurement and Modeling of Computer Systems, pp , Apr S. S. Lam, S. Chow and D. K. Y. Yau, “ A lossless smoothing algorithm for compressed video ”, IEEE/ACM Transactions on Networking, pp , vol. 4, issue 5, Oct Scene based smoothing H. Liu, N. Ansari and Y.-Q. Shi, “ Dynamic bandwidth allocation for VBR video traffic based on scene change identification ”, Proceedings of International Conference on Information Technology: Coding and Computing, pp , March Re-arranging sending sequence of frames R. Sabat and C. Williamson, “ Cluster-based smoothing for MPEG-based video-on- demand systems ”, IEEE International Conference on Performance, Computing and Communications, pp , Apr

16 Other Studies (cont.) Lossless online smoothing J. Rexford, S. Sen, J. Dey, W. Feng, J. Kurose, J. Stankovic and D. Towsley, “ Online Smoothing of Live, Variable-Bit-Rate Video ”, International Workshop on Network and Operating Systems Support for Digital Audio and Video, pp , May, Controlling online encoding parameters with: Buffer occupancy S. C. Liew and D. C.-Y. Tse, “ A control-theoretic approach to adapting VBR compressed video for transport over a CBR communications channel ”, IEEE/ACM Transactions on Networking, pp , vol. 6, issue 1, Feb Network status N. G. Duffield, K. K. Ramakrishnan, and A. R. Reibman, “ SAVE: an algorithm for smoothed adaptive video over explicit rate network ”, IEEE/ACM Transactions on Networking, pp , vol. 6, issue 6, Dec

17 Monotonic Decreasing Rate Scheduler Motivation Constructing an MDR Schedule Performance Evaluation Admission Complexity Waiting Time vs. System Utilization Buffer requirement

18 Motivation Existing smoothing algorithms contains both upward and download bandwidth changes Complex admission to provide deterministic performance guarantee Upward changes may fail in mixed traffic environments Solution: transmission with downward bandwidth changes only – MDR Scheduler

19 Constructing an MDR Schedule

20 Performance Evaluation 274 VBR encoded DVD videos tested Avg. bit-rate: 6.01Mbps Avg. length: s Round length: 1s Requests generated according to Poisson process to select a random video Un-admitted requests put to FIFO queue

21 Admission Complexity

22 Waiting Time vs. System Utilization

23 Buffer Requirement

24 Aggregated Monotonic Decreasing Rate Scheduler Principle Bandwidth Over-allocation Admission Complexity Performance Evaluation Effect of Network Topology

25 Principle Specify a buffer requirement, B For streams with buffer requirement: <= B, deliver with MDR schedules > B, deliver with optimal smoothing and over-allocate bandwidth to maintain monotonicity of the aggregate system traffic

26 Bandwidth Over-allocation + + = = New exceptional stream, smoothed using optimal smoothing. Current aggregate bandwidth utilization. Aggregate bandwidth utilization and reservation after new stream is admitted. Bandwidth over-allocated here to maintain ratemonotonicity. Time Rate

27 Admission Complexity Unsuccessful admission comparisons = additions: O(  +(1  )(g+1)) = O(1+(1  )g) Successful admission comparisons: O(  +(1  )(g+1+w)) = O(1+(1  )(g+w)) additions: O(w) Where:  is the proportion of videos served by MDRS g is the no. of bit-rate increases in optimal smoothing

28 Admission Complexity (cont.) With 16M of client buffer

29 Admission Complexity (cont.) With 32M of client buffer

30 Admission Complexity (cont.) With 64M of client buffer

31 Waiting Time vs. Client Buffer Size (cont.)

32 Waiting Time vs. Client Buffer Size

33 Waiting Time vs. System Capacity To be completed …

34 Effect of Network Topology In practice, network topologies are likely to be more complex We simulate a network with two-level, tree-based topology The effect of maintaining monotonicity within each individual branch is studied Results: to be completed …

35 Conclusions Scheduling of VBR video streaming is a complex problem Smoothing can reduce the variability; but will not completely solve the problem The MDR Scheduler can provide deterministic guarantee with low admission complexity Performance is comparable optimal smoothing With a trade off in performance and complexity, the AMDR Scheduler adapt to any buffer size

36 Q&A Thank you