School of Computing Science Simon Fraser University, Canada

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

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 2

3. Mobile TV • Batterypowered • Mobile, wireless • Small screens, ...

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 4

5. 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)

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 Bit Rate Burst R Off r1 Time 6

7. Burst Transmission Schedule Problem Bit Rate • 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 R Time Frame p

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

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 Bit Rate R Time Frame p 9

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 10

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 11

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 12

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 13

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 14

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

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

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 17

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

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 19

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 20

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

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 22

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