1 Bridging Content-Pipe Divide Amitabha Ghosh Haris Kremo Jiasi
Chen Josphat Magutt April 28, 2011
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2 Agenda Content-Pipe Divide Content-Aware Networking Video
Over Wireless Implementation (Theory vs. Practice) Quota-Aware
Video Adaptation
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3 Content-Pipe Divide Content Side Media companies: own video
and music End-users: post video online Operators of CDN and P2P
systems Seek the best way to distribute content Through multimedia
signal processing, caching, relaying, sharing, Treat network as
just a means of transportation Seek the best way to manage network
infrastructure Through resource allocation on each link, between
links, and end-to-end Treat content as just bits to transport
between nodes Pipe Side ISPs Equipment vendors Network management
software vendors Municipalities and enterprises DIVIDEDIVIDE
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4 Traditional Thinking Separation between content generation
and transportation Transcode Generate multimedia Frames Shaping
Queuing Marking Dropping Transportation network Separation
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5 New Thinking Content-Aware Networking Adjust PHY and MAC
layer parameters to suit Drop packets by frame distortion (I, P vs.
B) Network-Aware Content Generation SVC transcoding Joint
summarization + resource allocation GOP: IPBBPBBPBB
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6 Rate-Distortion Fair Two flows competing for BW over a common
link Rate Fairness: Each flow gets half the capacity Distortion
Fairness: Flow1 gets more capacity than Flow2 Flow1 with less
motion helps Flow2 with rich motion
8 Related Works Content-Aware distortion-Fair dropping [Chiang
09] Minimize max end-to-end distortion in multi-hop wired networks
User-driven, threshold-based dropping based on frame priorities
Discrete time frame selection [Chiang 08] Voice + video, wireless,
one-hop, multi-user JARS: Joint Adaptation (summarization),
Resource allocation (distributed pricing-based), Scheduling (greedy
centralized TDM) MU-MDP traffic state optimization [van der Schaar
10] Maximize expected discounted accumulated utility Buffer
modeling, value iteration, reinforcement learning, Bellmans
equations, stochastic sub-gradient
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9 Related Works Modulation, MAC retry, path selection [van der
Schaar 06] Cross-layer approach to maximize capacity-distortion
utility Exhaustive search, greedy algorithm Rate-distortion
optimized streaming [Chou 06] Single user, wired network Scheduling
policy vector to minimize expected distortion subject to rate
constraint Media-aware rate allocation [Girod 10] Proxy-server:
receiver-driven, proxy-client: sender-driven Policy (Markov
decision tree): which packets to select for transmission Iterative
Sensitivity Analysis (ISA)
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10 Problem Formulation CDMA Uplink: An Implementable Solution :
TX power of user i at time t : SINR at BS from user i at time t
Rate: Utility: negative distortion Goal: subject to: SINR and
deadline constraints Scheduling vs. Power Control CSMA vs.
CDMA
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11 Implementation Theory vs. Practice
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12 Closed loop power control for CSMA driven by video quality A
software defined radio implementation study Haris Kremo
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13 Outline Implementation Power control algorithm target
received power driven by video quality requires video profiling
received signal strength (RSSI) feedback Demo setup Conclusion on
theory vs. practice gap
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14 Rice University WARP software defined radio PHY: 802.11
(p-like) OFDM 64 carriers across 10 MHz transmit power adjustable
in 0.5 dB steps range: -20 dBm to 10 dBm BPSK, QPSK, 16-QAM, 64-QAM
MAC: 802.11 DCF carrier sensing through energy detection
exponential random backoff ACK successful reception programmable
Xilinx FPGA
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15 Closed loop power control Select signal strength at receiver
to match desired video quality Adjust transmit power to achieve
that signal strength PSNR to RSSI target PSNR receiver - DATA ACK
calculate RSSI transmitter i receiver j time varying channel
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16 Video profiling Tabulate distortion vs. signal strength
Connect transmitter and receiver with a cable For different fixed
power levels in 2dBm steps: stream video and save it on the
receiver record RSSI calculate frame-by-frame distortion offline
original video distortion fixed adjustable power received video
RSSI
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17 Experimental setup Four videos streaming to one receiver
High Definition (HD) vs. Low Definition (LD) High Motion (HM) vs.
Low Motion (LM) Adjust manually target PSNR HDHM HDLM LDHM
LDLM
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18 Theory vs. practice CDMA vs. CSMA licensed vs. unlicensed
band connection based vs. packet based Hard to calculate video
metric in real time RSSI not a good measure of interference
Practicalities inaccuracies: 1dB resolution nonlinearities: set
power out of range outdated feedback:insufficient packet rate
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19 Quota-Aware Video Adaptation Jiasi Chen April 28, 2011
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20 System Architecture End UserEdge ISPInternetContent Provider
cost distortion of videos Video Stores multiple precoded streams of
each video
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21 Motivation Whats the best way to compress videos and stay
within budget constraints, while maintaining perceptual
quality?
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22 Adaptation Engine Algorithm Input video Classifier Output
video Quota User profile Video profile Profiler
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23 User Profiling
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24 Optimization Problem Maximize utility Subject to budget
constraints Special case of knapsack problem Online algorithm:
video requests are not known in advance As each request arrives,
make an on-the- fly decision of how much to compress
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25 Divide billing cycle into sessions In each session, create a
knapsack based on prediction Choose items for knapsack Optimal to
of offline algorithm (Chakrabarty et al., Budget constrained
bidding in keyboard auctions and online knapsack problems, Proc. 17
th Intl Conf WWW, 2008) Online Algorithm
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26 Online Algorithm
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27 Consumer Cost Savings DataCost First 200 MB$15 Each
additional 200 MB$15 Quota = 200 MB
