Mohamed Hefeeda 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

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

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Mobile TV

Batterypowered Mobile, wireless Small screens, ...

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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

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

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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

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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

Mohamed Hefeeda

The Need for Different Bit Rates

Wide variations in quality (PSNR), as 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

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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

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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

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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

1 2 , 0,1,2,icr r i

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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

1 2 Sr rr

12kR r *p

*p 1/R r *1p r

1/sr r*

1// ( )sp r r

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Four TV channels: Medium bandwidth: is divided into 8 bursts

P2OPT: Example

1 2 3 4256, 512, 1024 kbpsr r rr

1kbps2048 8R r

Build binary tree, bottom up Traverse tree root-down to

assign bursts

*p

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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

( log )O S S

*1/p b r

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Complete open-source implementation of testbed for DVB-H networks: base station, web GUI, analyzers

P2OPT: Empirical Validation

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P2OPT is implemented in the Time Slicing module

P2OPT: Empirical Validation

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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

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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

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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

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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

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Quality gap < 1 dB P2OPT is useful in practice

P2OPT: Quality Variation

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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

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Thank You!

Questions??

Details are available in our papers at:

http://nsl.cs.sfu.ca/