Transcript Ph.D. Proposal - University of Southern California

Bridging Content-Pipe Divide
Amitabha Ghosh
Haris Kremo
Jiasi Chen
Josphat Magutt
April 28, 2011
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Agenda
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
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Content-Pipe Divide
Content-Aware Networking
Video Over Wireless
Implementation (Theory vs. Practice)
Quota-Aware Video Adaptation
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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
Pipe Side


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D
I
V
I
D
E
ISPs
Equipment vendors
Network management software
vendors
Municipalities and enterprises
 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
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Traditional Thinking

Separation between content generation and
transportation
Transportation network
Generate
multimedia
Transcode
Frames
Shaping
Queuing
Marking
Dropping
Separation
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New Thinking
GOP: IPBBPBBPBB

Content-Aware Networking
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Adjust PHY and MAC layer parameters to suit
Drop packets by frame distortion (I, P vs. B)
Network-Aware Content Generation
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SVC transcoding
Joint summarization + resource allocation
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Rate-Distortion Fair
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
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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
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Distortion Metric

PSNR

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Captures only spatial variation
PCA

Captures motion/activity
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Related Works
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Content-Aware distortion-Fair dropping
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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


[Chiang ‘09]
[van der Schaar ‘10]
Maximize expected discounted accumulated utility
Buffer modeling, value iteration, reinforcement learning, Bellman’s
equations, stochastic sub-gradient
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Related Works

Modulation, MAC retry, path selection
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Cross-layer approach to maximize capacity-distortion utility
Exhaustive search, greedy algorithm
Rate-distortion optimized streaming

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[Chou ‘06]
Single user, wired network
Scheduling policy vector to minimize expected distortion subject to
rate constraint
Media-aware rate allocation

[van der Schaar ‘06]
[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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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:

Goal:

negative distortion
subject to: SINR and deadline constraints
 Scheduling vs. Power
Control
 CSMA vs. CDMA
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Implementation
Theory vs. Practice
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Closed loop power control for CSMA
driven by video quality
A software defined radio implementation study
Haris Kremo
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Outline
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Implementation
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Power control algorithm
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target received power driven by video quality
requires video profiling
received signal strength (RSSI) feedback
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Demo setup
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Conclusion

on theory vs. practice gap
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Rice University WARP software defined radio
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PHY: 802.11 (“p”-like) OFDM
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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
programmable
Xilinx FPGA
MAC: 802.11 DCF


carrier sensing through energy detection
exponential random backoff
 ACK successful reception
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Closed loop power control
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
Select signal strength at receiver to match desired video quality
Adjust transmit power to achieve that signal strength
Δ𝑃 = 𝑅𝑆𝑆𝐼𝑡𝑎𝑟𝑔𝑒𝑡 − 𝑅𝑆𝑆𝐼measured
target
PSNR
PSNR to
RSSI
receiver
time varying channel
𝑃𝑖 (𝑡)
DATA
𝐺𝑖𝑗 (𝑡)
receiver j
ACK
transmitter i
piggyback
𝑃𝑖 𝑡 𝐺𝑖𝑗 (𝑡)
calculate
RSSI
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Video profiling

Tabulate distortion vs. signal strength
original
video
received
video
fixed adjustable
power
RSSI
distortion


Connect transmitter and receiver with a cable
For different fixed power levels in 2dBm steps:
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
stream video and save it on the receiver
record RSSI
calculate frame-by-frame distortion offline
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Experimental setup

Four videos streaming to one receiver
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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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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:
nonlinearities:
outdated feedback:

…


1dB resolution
set power out of range
insufficient packet rate
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Quota-Aware Video Adaptation
Jiasi Chen
April 28, 2011
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System Architecture
Edge ISP
Internet
Content Provider

distortion of videos
End User
Stores multiple
precoded streams of
each video
Video
cost
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Motivation
What’s the best way to compress videos and stay
within budget constraints, while maintaining
perceptual quality?
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Adaptation Engine
Profiler
Quota
User profile
Algorithm
Input video
Classifier
Video profile
Output video
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User Profiling
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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-thefly decision of how much to compress
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Online Algorithm

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. 17th
Intl Conf WWW, 2008)
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Online Algorithm
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Consumer Cost Savings

Quota = 200 MB
Data
Cost
First 200 MB
$15
Each additional 200 MB
$15
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Thank you!
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