Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Energy Optimization in...
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![Page 1: Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Energy Optimization in Mobile TV Broadcast Networks Mohamed Hefeeda (Joint.](https://reader035.fdocuments.in/reader035/viewer/2022062308/56649d2d5503460f94a03250/html5/thumbnails/1.jpg)
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
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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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Mohamed Hefeeda 3
Mobile TV
Batterypowered Mobile, wireless Small screens, ...
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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
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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)
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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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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
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Time
R
Bit Rate
Frame p
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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/