A Layered Hybrid ARQ Scheme for Scalable Video Multicast over Wireless Networks
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Transcript of A Layered Hybrid ARQ Scheme for Scalable Video Multicast over Wireless Networks
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A Layered Hybrid ARQ Scheme for Scalable Video Multicast
over Wireless Networks
Zhengye Liu,
Joint work with
Zhenyu Wu
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Outline
Motivations & challenges Review of error protection approaches Layered hybrid ARQ Operating point selection in multiple user
scenario A general game theoretic framework in operating
point selection Layered hybrid ARQ in video multicast
Conclusion
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Motivations & Challenges
Motivation of video multicast over WLANs Utilize bandwidth efficiently
Challenges Error protection mechanisms are needed
Fading, channel interference, … Heterogeneity of channel conditions
Different channel conditions Overall system performance Individual user fairness
S
C C C
RR
RS
C CC
R
unicast multicast
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Packet Loss Pattern
Burst packet losses Difficult to predict
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Review of error protection approaches
Retransmission Inappropriate in multicast scenario
FEC Constant throughput and bounded delay Throughput is reduced in the good state
Adaptive FEC Prediction of channel conditions in the future
Hybrid ARQ [Majumdar 02]
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Hybrid ARQ Scheme Generate parity packets Send source packets Send parity packets until all lost source packets can be
recovered
S 1 2 3 4 5 1 2 3
C
ACK
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Hybrid ARQ Scheme
Use bandwidth efficiently Should have sufficient bandwidth
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Layered Hybrid ARQ Scheme
Encode a video into multiple layers Temporal scalability
Transmit packets from more important layers to less important layers
For each layer, transmit source packets first and then parity packets, based on hybrid ARQ
Given a total transmission bandwidth, provide unequal protection Protect more important layers Selectively drop source packets from less important layers No overall rate expansion
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An Illustration
S
C
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Performance Evaluation
Single user scenario Only one user in the multicast group
Comparison Hybrid ARQ with single layer video (single hybrid
ARQ) Layered hybrid ARQ
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Simulation Setup (1)
H.264 codec JM11 Football (720x480, 30 frame/sec) Average bitrate: 1400 kbps
Fix QPs
Temporal scalability in H.264
Layer 1 Layer 2 Layer 3
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Simulation Setup (2)
RS coding (255, k) for each layer Frame copy in decoder Total transmission bandwidth: 2200 kbps Packet loss pattern: two-state Markov model
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Packet Receive Ratio Percentage of received/recovered source packets over the total
encoded source packets
Layered hybrid ARQ can provide unequal protection for different layers
All packets from I and P frames can be received Most packets from Bs frames can be received
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Average Channel Induced MSE
Layered hybrid ARQ can outperform hybrid ARQ significantly in received video quality
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MSE Frame by Frame (p=30%)
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Demo
Packet loss rate: 30% Single hybrid ARQ vs. Layered hybrid ARQ
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Layered Hybrid ARQ in Multiple User Scenario
Heterogeneity of channel conditions Different preferred configurations (operating
points) of video multicast
S
C2C1
?
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How to Select Operating Point?
Worst case Based on the user with the worst channel condition
Play a game Play “lottery” among users
C1 C2
Parity packet from layer 1
Source packet from layer 2
50% 50%
C1 C2
Parity packet from layer 1
Source packet from layer 2
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Is This Game Fair? Two players, each owning a car, play lottery with each
other If a player wins the game, he/she can win the car from
the other player
Player 1 Player 2
50% vs. 50%? 99% vs. 1%
What are the probabilities for a fair game?
C1 C2
Parity packet from layer 1
Source packet from layer 2
λ1 vs. λ2 ?
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Nash Bargaining Game
Proposed by John Nash in 1950 A cooperative game
Players have perfect knowledge of each other
Proved the existence of Nash bargaining solution (NBS) for this game Unique solution Pareto optimal
No other solution produces better utility for one player without hurting another player
Fair in the sense of cooperative game Satisfy the axioms of fairness
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Formulation of Nash Bargaining Game Player:
N users in a multicast group Strategy:
M operating points, sm
Mixed game with mixed strategy S = λ1s1 + λ2s2 +,…,+ λMsM
Preference: The utility of each strategy for user i, ui(sm). Mixed utility
Ui = λ1ui(s1) + λ2ui(s2) +,…,+ λMui(sM) Initial utility:
di, user would like to at least achieve if they enter the game Ui>di, otherwise user i will not enter the game
Nash bargaining solution (NBS): λ*=(λ1, λ2,…, λM) Users consider it as a fair setting of the lottery
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An Example
Player: Two users
Strategy: Three operating points, sm=“transmit a packet from layer m” Mixed game with mixed strategy, pm is the probability that a packet from layer m
will be chosen S = λ1s1 + λ2s2 + λ3s3
Preference: ui(sm): how much payoff user i can get when a packet from layer m will be sent The anticipation of payoff from the lottery (mixed game)
Ui = λ1ui(s1) + λ2ui(s2) + λ3ui(s3) Initial utility:
di
C1 C2
Parity packet from layer 1
Source packet from layer 2
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Utility
If user i is requesting layer m, then only a packet from layer m is useful.
wm should represent the importance of a packet from layer m on video quality Use a channel distortion model to obtain wm
C1Parity packet from layer 1 u1(s1) = w1, u1(s2) = 0, u1(s3) =0
C2Source packet from layer 2 u2(s1) = 0, u2(s2) = w2, u2(s3) =0
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Channel Distortion Model of Temporal Scalable Video Channel distortion model of single layer video
Channel distortion model of temporal scalable video
w1=1400, w2=650, w3=150
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Initial Utility
Guarantee that the expected Ui>di
A flexible control parameter Select a higher di, if the “system” gives more
protection to user i User i subscribes more premium service It is more urgent for user i to win the game
If user i is requesting a packet from layer m
C1Parity packet from layer 1 C2
Source packet from layer 2
d1>d2
α=2 d1=w1/2,d2=w2/4
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Obtain NBS
A Nash bargaining game Player, strategy, preference (utility), and initial utility
Solve an optimization problem
Exhaustive search for small M Convex programming for a large M
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Procedure of operating point selection
Trace the state for each user From which layer the user is requesting a packet Based on the ACKs sent from the receivers
Play Nash bargaining game Obtain ui(sm) and di
Obtain the NBS λ*=(λ1, λ2,…, λm) Given λ*, play lottery to select a packet for
sending
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Performance Evaluation (1)
Lead to NBS optimality and fairness in microscopic view (packet level)
The macroscopic affect of a strategy on received video qualities Overall performance: The majority of users are more
likely to obtain their preferred operating points than the minority of users
Individual fairness: No individual user is denied access to the multicasting system or overly penalized
Flexibility: Can be tuned to satisfy different requirements.
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Performance Evaluation (2)
Comparison Worst case Nash bargaining game
Investigate the impact of initial utility di on system performance
Higher di leads to more protection to user i α=2, 4, and 100 By using a smaller α, guarantee a better basic video quality
for bad channel users
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Simulation Setup
N users totally N-1 users are in a good channel condition
(p=1%) One user is in a bad channel condition (p=30%) N=2, 4, 8, 16, 32
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Average MSE
(a) Good channel user (b) Bad channel user
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Summery
Worst case Nash bargaining
Overall performance
Ignore the majority of users
Adapt the operating point to the majority of users
Individual
fairness
The good channel users are overly penalized
No user is overly penalized
Flexibility Change α to satisfy different requirements
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Conclusion
Propose a layered hybrid ARQ scheme for video delivery over WLANs
Propose a game theoretic framework in operating point selection for video mulitcast
Examine the game theoretic framework with the proposed layered hybrid ARQ
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Thanks!