1. Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Energy Optimization in Mobile TV Broadcast Networks Mohamed Hefeeda (Joint work with ChengHsin Hsu) 16 December 2008

2. Mohamed Hefeeda 2  Most mobile devices (phones, PDAs,...) are almost full-fledged computers  Users like to access multimedia content anywhere, anytime  Longer Prime Time viewing  More business opportunities for content providers  Market research forecasts (by 2011) -500 million subscribers, 20 billion Euros in revenue  Already deployed (or trial) networks in 40+ countries [http://www.dvb-h.org] Mobile TV: Market Demand & Potential

3. Mohamed Hefeeda 3 Mobile TV  Batterypowered  Mobile, wireless  Small screens,...

4. Mohamed Hefeeda 4  Over (current, 3G) cellular networks -Third Generation Partnership Project (3GPP)  -Multimedia Broadcast/Multicast Service (MBMS) -Pros: leverage already deployed networks -Cons: Limited bandwidth (<1.5 Mb/s)  very few TV channels, low quality, and high energy consumption for mobile devices (they work mostly in continuous mode) Mobile TV: Multiple Technologies

5. Mohamed Hefeeda Mobile TV: Multiple Technologies  Over Dedicated Broadcast Networks -T-DMB: Terrestrial Digital Media Broadcasting Started in South Korea Builds on the success of Digital Audio Broadcast (DAB) Limited bandwidth (< 1.8 Mbps) -DVB-H: Digital Video Broadcast—Handheld Extends DVB-T to support mobile devices High bandwidth (< 25 Mbps), energy saving, error protection, efficient handoff, … Open standard -MediaFLO: Media Forward Link Only Similar to DVB-H, but proprietary (Qualcomm) 5

6. Mohamed Hefeeda 6  This is called Time Slicing -Supported (dictated) in DVB-H and MediaFLO -Performed by base station to save energy of mobile receivers -Also enables seamless hand off  Need to construct Burst Transmission Schedule Energy Saving for Mobile TV Receivers Time Bit Rate R r1 Off Burst

7. Mohamed Hefeeda Burst Transmission Schedule Problem  Easy IF all TV channels have same bit rate -Currently assumed in many deployed networks Simple, but not efficient (visual quality &bw utilization) TV channels broadcast different programs (sports, series, talk shows, …)  different visual complexity/motion 7 Time R Bit Rate Frame p

8. Mohamed Hefeeda The Need for Different Bit Rates  Wide variations in quality (PSNR), a s high as 10—20 dB  Bandwidth waste if we encode channels at high rate 8 10 dB  Encode multiple video sequences at various bit rates, measure quality

9. Mohamed Hefeeda 9  Ensure no buffer violations for ALL TV channels -Violation = buffer underflow or overflow  Ensure no overlap between bursts Burst Scheduling with Different Bit Rates Time R Bit Rate Frame p

10. Mohamed Hefeeda 10  Theorem 1: Burst Scheduling to minimize energy consumption For TV channels with arbitrary bit rates is NP-Complete  Proof Sketch: -We show that minimizing energy consumption is the same as minimizing number of bursts in each frame -Then, we reduce the Task Sequencing with release times and deadlines problem to it  We can NOT use exhaustive search in Real Time Burst Scheduling with Different Bit Rates

11. Mohamed Hefeeda 11  Practical Simplification: -Divide TV channels into classes -Channels in class c have bit rate: -E.g., four classes: 150, 300, 600, 1200 kbps for talk shows, episodes, movies, sports -Present optimal and efficient algorithm (P2OPT)  For the General Problem -With any bit rate -Present a near-optimal approximation algorithm (DBS) Theoretical (small) bound on the approximation factor  All algorithms are validated in a mobile TV testbed Solution Approach

12. Mohamed Hefeeda 12  Assume S channels:  Also assume medium bandwidth  Compute the optimal frame length  Divide into bursts, each bits  Then assign bursts to each TV channel s  Set inter-burst distance as P2OPT Algorithm: Idea

13. Mohamed Hefeeda 13  Four TV channels:  Medium bandwidth:  is divided into 8 bursts P2OPT: Example  Build binary tree, bottom up  Traverse tree root-down to assign bursts

14. Mohamed Hefeeda 14  Theorem 2: P2OPT is correct and runs in. -i.e., returns a valid burst schedule iff one exists -Very efficient, S is typically < 50  Theorem 3: P2OPT is optimal when -Optimal = minimizes energy consumption for receivers -b is the receiver buffer size P2OPT: Analysis

15. Mohamed Hefeeda 15  Complete open-source implementation of testbed for DVB-H networks: base station, web GUI, analyzers P2OPT: Empirical Validation

16. Mohamed Hefeeda 16  P2OPT is implemented in the Time Slicing module P2OPT: Empirical Validation

17. Mohamed Hefeeda 17  Setup: Broadcast 9 TV channels for 10 minutes -4 classes: 2 @ 64, 3 @ 256, 2 @ 512, 2 @ 1024 kbps -Receiver Buffer = 1 Mb -Collect detailed logs (start/end of each burst in msec) -Monitor receiver buffer levels with time -Compute inter-burst intervals for burst conflicts P2OPT: Correctness

18. Mohamed Hefeeda 18  Never exceeds 1 Mb, nor goes below 0 P2OPT: Correctness TV Channel 1  No overlap, all positive spacing  And P2OPT runs in real time on a commodity PC Bursts of all TV Channels

19. Mohamed Hefeeda 19  Compare energy saving against absolutemaximum -Max: broadcast TV channels one by one, freely use the largest burst  max off time  max energy saving -P2OPT: broadcast all TV channels concurrently P2OPT: Optimality

20. Mohamed Hefeeda 20  Does encoding channels with power of 2 increments bit rate really help?  We encode ten (diverse) sequences using H.264: -Uniform: all at same rate r (r varies 32 -- 1024 kbps) -P2OPT: at 3 different bit rates P2OPT: Quality Variation

21. Mohamed Hefeeda 21  Quality gap < 1 dB  P2OPT is useful in practice P2OPT: Quality Variation

22. Mohamed Hefeeda 22  Energy saving: critical problem for mobile TV  TV channels should be encoded at different bit rates -Better visual quality, higher bandwidth utilization -BUT make burst transmission scheduling NP-Complete  Proposed a practical simplification -Classes of TV channels with power of 2 increments in rate -Optimal algorithm (P2OPT) and efficient  General Problem -Near-optimal algorithm (DBS): approx factor close to 1 for typical cases  Implementation in real mobile TV testbed Conclusions

23. Mohamed Hefeeda 23 Thank You! Questions??  Details are available in our papers at: http://nsl.cs.sfu.ca/