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
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Mobile TV: Market Demand & Potential
 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]
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Mobile TV
 Batterypowered
 Mobile, wireless
 Small screens, ...
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Mobile TV: Multiple Technologies
 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)
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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)
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Energy Saving for Mobile TV Receivers
Bit Rate
Burst
R
Off
r1
Time
 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
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Burst Transmission Schedule Problem
Bit Rate
R
Frame p
Time
 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
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The Need for Different Bit Rates
 Encode multiple video sequences at various bit rates,
measure quality
10 dB
 Wide variations in quality (PSNR), as high as 10—20 dB
 Bandwidth waste if we encode channels at high rate
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Burst Scheduling with Different Bit Rates
Bit Rate
R
Time
Frame p
 Ensure no buffer violations
for ALL TV channels
- Violation = buffer
underflow or overflow
 Ensure no overlap
between bursts
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Burst Scheduling with Different Bit Rates
 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
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Solution Approach
 Practical Simplification:
- Divide TV channels into classes
i
- Channels in class c have bit rate: rc  r1  2 , i  0,1, 2,
- 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
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P2OPT Algorithm: Idea






Assume S channels: r1  r2   rS
Also assume medium bandwidth R  2k  r1
*
Compute the optimal frame length p
*
*
p
Divide p into R / r1 bursts, each r1 bits
Then assign rs / r1 bursts to each TV channel s
*
Set inter-burst distance as p / (rs / r1 )
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P2OPT: Example
 Four TV channels: r1  r2  256, r3  512, r4  1024 kbps
 Medium bandwidth: R  2048 kbps  8 r1
*
 p is divided into 8 bursts
 Build binary tree, bottom up
 Traverse tree root-down to
assign bursts
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P2OPT: Analysis
 Theorem 2: P2OPT is correct and runs in O( S log S ) .
- i.e., returns a valid burst schedule iff one exists
- Very efficient, S is typically < 50
 Theorem 3: P2OPT is optimal when p*  b / r1
- Optimal = minimizes energy consumption for receivers
- b is the receiver buffer size
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P2OPT: Empirical Validation
 Complete open-source implementation of testbed for
DVB-H networks: base station, web GUI, analyzers
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P2OPT: Empirical Validation
 P2OPT is implemented in the Time Slicing module
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P2OPT: Correctness
 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
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P2OPT: Correctness
Bursts of all TV Channels
TV Channel 1
 Never exceeds 1 Mb, nor
goes below 0
 No overlap, all positive
spacing
 And P2OPT runs in real time on a commodity PC
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P2OPT: Optimality
 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
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P2OPT: Quality Variation
 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
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P2OPT: Quality Variation
 Quality gap < 1 dB  P2OPT is useful in practice
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Conclusions
 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
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Thank You!
Questions??
 Details are available in our papers at:
http://nsl.cs.sfu.ca/
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