Transcript 投影片 1 - CESG

Broadcasting Delay-Constrained Traffic
over Unreliable Wireless Links with
Network Coding
I-Hong Hou and P.R. Kumar
1
Wireless Broadcasting: Video Streaming
2
Application Characteristics
Traditional
Applications

No per-packet delay
bounds

Need to delivery
every packet
correctly
3
Video
Streaming


Strict per-packet delay
bounds
 Expired packets are
not useful
Can tolerate a small
amount of packet losses
Performance in the Future
High Throughput ≠
4
Performance in the Future
High Timely Throughput =
Timely Throughput: Throughput of
packets that are delivered on time
5
Challenges from Wireless Transmissions
Wireless transmissions are subject to shadowing, fading,
and interference
Therefore, wireless transmissions are unreliable


6
Challenges from Wireless Broadcast
ACKs are not implemented in broadcast
Costly to obtain feedbacks from all clients
No per-transmission feedback information



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Challenges from Wireless Broadcast
ACKs are not implemented in broadcast
Costly to obtain feedbacks from all clients
No per-transmission feedback information



8
System Model for Wireless Broadcast
with Delay Constraints
9
Client-Server Model
Clients
Flows
A
B
1
C
AP
2
3
10
Timeline
Traffic Model
A
B
1
C
Packet
Generation
AP
2
Interval
3
11
Traffic Model
A
B
1
C
AP
A
B
C
C
2
B
3
12
Model for Delay Constraints
A
B
1
C
Packet
Generation
Deadline
AP
2
Interval
3
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Model for Delay Constraints
Delays of delivered
packets are no larger than
the length of an interval
A
B
A,C expire
AP
2
A
C
1
C
Interval
3
14
Model for Unreliable Broadcast
Client n receives each
transmission successfully
with prob. pn
A
B
C
p1
AP
A
B
C
C
1
p2
2
p3
B
3
15
Scheduling Example
A
B
C
p1
A
AP
A
B
C
C
A
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1
A
2
X
3
X
p2
p3
B
Scheduling Example
Duplicate Packets are ignored
A
B
C
p1
A
AP
A
B
C
C
A A
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1
A
2
X X
3
X
A
X
p2
p3
B
A
Scheduling Example
A
B
C
p1
C
AP
A
B
C
C
A A C C
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1
A
2
X X C C
3
X
A
X
X X B
C
p2
p3
B X
B
B
C
A
C X X
X
Timely Throughput
Delivered
Timely
Throughput
A
B
C
1
0.5 0.5 0.5
2
0
3
0.5 0
0.5 1.0
p1
0.5
AP
1
A
2
X X C C
3
X
X X B
C
p2
p3
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A
X
A
B X
C X X
X
Timely Throughput
Delivered
Timely
Throughput
A
B
C
1
0.5 0.5 0.5
2
0
3
0.5 0
0.5 1.0
0.5
Required
Timely
Throughput
A
1
qA,1
qB,1
qC,1
2
qA,2
qB,2
qC,2
3
qA,3
qB,3
qC,3
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B
p1
C
AP
1
A
2
X X C C
3
X
A
X
X X B
C
p2
p3
A
B X
C X X
X
Timely Throughput Requirements
Timely
Throughput
A
B
C
1
0.5 0.5 0.5
2
0
3
0.5 0
A
0.5 1.0
0.5
AP
p1
1
A
A
p2
2
Required A
B
C
1
qA,1
qB,1
qC,1
2
qA,2
qB,2
qC,2
3
qA,3
qB,3
qC,3
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A
B
A
X
C
X
p3
3
C
B
X
C
X
X
C
B
A
C
C
X
B
B
C
C
C
B
B
X
C
X
B
X
X
Summary of Model




Flows have strict per-packet delay bound
Clients have timely throughput requirements on each
flow
Wireless transmissions are unreliable
AP does not have feedback information
Goal:
 Design policies to fulfill timely throughput requirements
for all flows and all clients as long as they are feasible
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Scheduling Policies
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Packet Deliveries
Delivery Debt
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Required
Actual
Delivery Debt
0
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Slope = qA,1
2
4
Time
6
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Expected Delivery Debt



AP does not have feedback information
But, AP can estimate packet deliveries
Expected delivery debt for client n and flow i at the kth
interval
di,n(k):= kqi,n-E{# of packets client n receives from flow i}
Client n receives A with probability 1-(1-pn)2,
and receives B with probability pn
AP
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A
A
B
A Framework for Designing Policies

Policy: Maximize
∑di,n(k)+Prob(client n receives a packet from flow i)
in every interval

Theorem:
This policy fulfills a system as long as it is feasible

Feasibility Optimal Policy
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A Policy without Coding

Marginal Delivery Probability (mi,n):
prob. that client n receives a new packet from flow i in a
particular transmission

Greedy Algorithm:
schedule the flow i that maximizes ∑ndi,n(k)+mi,n in every
time slot
mA,n =pn
mA,n =pn(1-pn)
mA,n =pn(1-pn)2
AP
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A
A
A
Optimality Result

Greedy Algorithm is feasibility optimal

Polynomial complexity per interval

However, it is only optimal among policies that do not
employ network coding
Can we improve performance by
employing network coding?
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Network Coding: XOR Coding
Client cannot obtain packet A
Duplicate Packet
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X
X
X
B
B
X
A
A
A
B
B
B
1
AP
Network Coding: XOR Coding

XOR Coding:
AP can broadcast
packets contain A, B, or
Client obtains both packets
AB
A=B  (A  B)
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X
X
X
B
AB
A
A
B
B
AB AB
X
1
AP
Pairwise XOR Policy

Design of Pairwise XOR Policy:



Only allow pairwise XOR
Satisfy some mild restrictions derived from Greedy Algorithm
Theorem:
Pairwise XOR Policy is feasibility optimal among all
policies that satisfy the mild restrictions.
Pairwise XOR Policy fulfills every system that can be
fulfilled without coding

Polynomial complexity per interval
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Network Coding: Linear Coding
Client cannot obtain packet A
Duplicate Packet
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X
X
X
B
B
X
A
A
A
B
B
B
1
AP
Network Coding: Linear Coding

Linear Coding:
AP broadcasts linear
combinations of packets
from flows
Client obtains both packets
B  ( A  5 B)  ( A  4 B)
A  ( A  4 B)  4* B
X
X
X
A+4B A+5B
X
A+B A+2B A+3B A+4B A+5B A+6B
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1
AP
Optimal Grouping Policy

Design of Optimal Grouping Policy:



AP broadcasts linear combinations of packets
Satisfy some mild restrictions derived from Greedy Policy
Theorem:
Optimal Grouping Policy is feasibility optimal among all
policies that satisfy the mild restrictions.
Optimal Grouping Policy fulfills every system that can be
fulfilled without coding

Polynomial complexity per interval
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Simulation Results
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VoIP Traffic

ITU-T G.711



Packet size = 160 Bytes
Interval length = 40 ms
IEEE 802.11b


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Transmission rate = 11 Mb/s
20 time slots in an interval
Network Topology



20 clients and one AP
AP broadcasts 10 flows
qi,n= α, for 1 ≤ i ≤ 5; qi,n= β, for 6 ≤ i ≤ 10
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Simulation Result
Plot all (α, β) that can be fulfilled by each policy
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Conclusion

Studied the problem of broadcasting delay-constrained
flows through wireless links

Proposed a model that jointly considers the following:




Per-packet delay bounds of flows
Timely throughput requirements of clients for each flow
Unreliable wireless transmissions
Lack of per-transmission feedbacks in broadcast

Proposed a policy that is feasibility optimal

Explored the usage of network coding to enhance
performance
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