On Statistical Multiplexing of VBR Video Streams in Mobile Systems

1. On Statistical Multiplexing of VBR Video Streams in Mobile Systems Cheng-Hsin Hsu Simon Fraser University, Canada joint work with Mohamed Hefeeda ACM Multimedia 09’, October 21st, 2009

2. Mobile Video Broadcast Networks • Base station concurrently transmits multiple video streams over a broadcast network to mobile devices • Design goals of base stations: • low energy consumption  long watch time • high goodput  more concurrent videos • feasible schedule  no buffer over/underflow

3. This is called Time Slicing (in broadcast standards) Need to construct feasible burst schedules no two bursts overlap with each other in time no receiver buffer over/underflow instances How do commercial base stations construct schedules? Energy Saving for Mobile Receivers Bit Rate Overhead To Burst R Off r Time

4. Commercial Base Stations • Such as UDCast IPE-10 and UBS DVE-6000 • Schedule bursts in Round-Robin fashion • operators manually choose a scheduling window size and burst sizes for all stream s • Transmit a video stream in its assigned time slot Slotted Scheduling Bit Rate R Time

5. Slotted Scheduling • Fine, if all video streams are encoded at the same and constant bit rate • But most videos are VBR and at different bit rates • choosing parameters manually is error-prone • RR is not efficient in terms of energy saving R Overhead To Time A more flexible scheduler is needed!

6. Problem Statement • Given multiple VBR streams, construct a feasible burst schedule for them to maximize energy saving and goodput • no burst overlaps, no buffer over/underflow instances • bursts are flexible in sizes and start times

7. Hardness • Theorem: burst scheduling to maximize energy saving is NP-Complete [ToN'09] • Proof Sketch: • We show that maximizing energy saving is the same as minimizing number of bursts • Then, we reduce the task sequencing problem with release times and deadlines problem to it • Corollary: scheduling to maximize goodput and energy saving is NP-Complete

8. Problem Formulation Goodput: amount of on-time delivered data over broadcast network capacity Energy saving: fraction of time mobile receivers can turn off their receiving circuits No two bursts overlap with each other No buffer underflow instances No buffer overflow instances The problem is NP-complete  approx algorithm

9. Observation • Hardness is due to tightly-coupled constraints: no burst overlap & no buffer under/overflow instances • machine scheduling algorithms may lead to playout glitches Buffer Fullness Buffer Fullness Buffer Fullness Time Time Time Buffer Underflow Buffer Overflow

10. Decouple These Two Constraints • Transform the formulation into a new one, in which any feasible schedule leads to no buffer violation instances in the original formulation • Solve the transformed formulation efficiently • only one constraint: no overlaps • Convert the schedule for the original formulation • Ensure correctness and bound optimality gap in all steps

11. Transform • Transform idea • divide receiver buffer into two: B and B’ • divide the broadcast time into multiple windows • in each window, drain data from B while filling B’ and vice versa • Goal of transformed formulation • schedule bursts, so that bits consumed in the current window = bits received in the preceding window

12. Buffer Dynamics Window 2 Window 1 Window 3 Fill Drain Buf B Fill Fullness Buf B’ Fill Drain Drain

13. Construct Windows • Windows are constructed using video traces • yp: required amount of received data in window p • compute the maximum number of frames can be included in B or B’ for higher energy saving • xp: start time of window p • zp: end time of window p • follow frame rate (fps) and number of frames sent in immediately previous window

14. Transformed Formulation  No buffer under/overflow instances

15. The SMS Algorithm // transform For each video stream, divide the broadcast time into multiple windows based on the frame sizes // note that each window is specified by <required burst length, start time, end time> // scheduling by decision points: (1) new window starts, (2) window completes, and (3) window ends For each decision point t, schedule a burst from time t to tnfor the window with the smallest end time, where tn is the next decision point

16. Analysis of the SMS Algorithm • Theorem [Correctness]: SMS gives feasible burst schedules • Theorem [Optimality]: SMS returns optimal schedules in terms of goodput • Theorem [Complexity]: SMS runs in time O(PS + S2), where S is the number of TV channels and P is the maximum number of windows

17. Analysis on the SMS Algorithm (cont.) • Theorem [Near-Optimality]: SMS returns near-optimal schedules in terms of energy saving • The approximation gap is: • , where r is the average bit rate across all video streams, and Q is receiver buffer size • How good is it?

18. Numerical Analysis on Near-Optimality energy saving achieved by SMS is at most 1.25% less than the optimum, if average rate = 512 kbps, buffer size > 1MB

19. Simulation Setup • Implemented a simulator for DVB-H networks • took H.264 VBR traces (from ASU) as input • Simulated 20 concurrent streams for 60 mins • Compared 3 algorithms: SMS, VBR, RVBR • VBR: directly broadcast VBR streams • RVBR: broadcast rate regulated VBR • Considered metrics: (i) fraction of missed frames, (ii) maximum number of streams, (iii) energy saving

20. Simulation Results: Missed Frames SMS results in almost no missed frame, while VBR and RVBR have up to 33% and 12% missed frames

21. Simulation Results: Number of Streams SMS allows broadcasting 20 streams, while RVBR and VBR allow 14 and less than 3

22. Simulation Results: Energy Saving SMS achieves energy saving 2—7% lower than a conservative upper bound (UB), and is better than VBR and RVBR for up to 12% and 5%

23. Implementation on Mobile TV Testbed • Implemented SMS in our mobile TV testbed • a Linux base station, protocol analyzer, and smart phones as receivers [MM’08Demo] • Encoded five videos (from CBC) into mp4 files with H.264 video and MPEG-4 AAC audio • Concurrently broadcast 20 mp4 files • Measured burst times to validate correctness and compute per-channel energy saving

24. Experimental Results: Energy Saving SMS achieves high energy saving in real testbed: about 80% for 768 kbps streams and 93% for 250 kbps ones

25. Conclusion • An efficient burst scheduling algorithm for transmitting VBR streams in broadcast networks • The algorithm is optimal on goodput and near-optimal on energy saving • Achieve high energy saving: at most 1.25% worse than optimum • Evaluated the algorithm with simulations and a real mobile TV testbed • good streaming quality, high goodput, and high energy saving

26. Thank You, and Questions? More details can be found online at http://nsl.cs.sfu.ca

27. Related Work • Joint rate control among TV channels [TMM’08] • assume joint encoders/transcoders are collocated with base station  expensive • fixed burst schedules  still RR • Statistical multiplexing without look-ahead windows [IJDMB’09] • predict the short-term VBR traffic pattern  nondeterministic next burst time • flexible burst lengths  still RR