A Non-Monetary Protocol for P2P Content Distribution in Wireless Broadcast

Networks with Network Coding

I-Hong Hou, Yao Liu, and Alex Sprintson

Dept. of ECE, TAMU

Wireless P2P

• Exchange data locally instead of getting all packets from the base station

A BA,B

A,B

Wireless P2P

• Exchange data locally instead of getting all packets from the base station

A BA,B

A,BA B

Wireless P2P

• Exchange data locally instead of getting all packets from the base station

A BA,B

A,BA B

A B

Wireless P2P

• Exchange data locally instead of getting all packets from the base station

A BA

B

A B

Wireless P2P

• Exchange data locally instead of getting all packets from the base station

A BA

B

A

B

Benefits of Wireless P2P

• Exchange data locally requires less power• Reduce power consumption• Reduce interference• Increase spatial reuse and hence total

system capacity

• Reduce the amount of data from BS• Reduce cost

Incentives in P2P

• People benefit from “receiving” data, not “transmitting” data

• A policy is needed to make people contribute• Tic-for-tac exchange between 2 peers

A BNeed: B Need: A

Incentives in P2P

• People benefit from “receiving” data, not “transmitting” data

• A policy is needed to make people contribute• Tic-for-tac exchange between 2 peers

A BNeed: B Need: A

A

B

Free Rider Problem

• The broadcast wireless channels make it a little bit more tricky…

A

A

B

BNeed: BNeed: A

…….

…….

Free Rider Problem

A

A

B

BA B

• The broadcast wireless channels make it a little bit more tricky…

• The broadcast wireless channels make it a little bit more tricky…

Free Rider Problem

A

A

B

BA

A

B

B Free Riders!

Contributions

• Propose a non-monetary protocol for wireless P2P networks

• Address the free rider by incentivizing nodes to contribute

• Derive closed-form Nash Equilibrium• Propose a distributed mechanism that

converges to the Nash Equilibrium• Incorporate network coding

A Model for Incentives

For every packet I download, I want:• Make as few transmissions as possible• Minimize the inter-packet delay, or,

equivalently, maximize download rate

A Model for Incentives

My cost function:

gn{avg. transmissions per download}

+wn{avg. inter-download delay}

gn: price for transmission

wn: price for waiting

Protocol Illustration

A

A

B

B

TimeTime

Protocol Illustration

A

A

B

B

backoff

backoff

TimeTime

Protocol Illustration

A

A

B

B

Time up

backoff

TimeTime

Have: A Need: B

Protocol Illustration

A

A

B

B

backoff

backoff

TimeTime

Protocol Illustration

A

A

B

B

backoff

Time up

TimeTime

B

Protocol Illustration

A

A

B

B

TimeTime

A

B

B

Protocol Illustration

A

A

B

B

TimeTime

B

B

A

A

Protocol for Bilateral File Exchange

• Two files, A and B, in the system• The protocol consists of rounds• In the beginning of a round, nodes need B

secretly picks a backoff time• The node n with the smallest backoff time

transmits a control packet after backoff• Nodes that need A secretly picks a backoff

time• The node m with the smallest backoff time

exchanges with n

Why the Protocol Works?

For every packet I download, I want:• Make as few transmissions as possible• Minimize the inter-packet delay, or,

equivalently, maximize download rate

If I pick a large backoff time• More likely that I don’t transmit

• Longer delay

Performance Analysis

• Only two files in the system• Strategy of node n that needs A: Choose

backoff time as an exponential variable with mean

• Theorem: This strategy is a Nash Equilibrium• Average amount of time on backoff:

Performance Analysis

• Only two files in the system• Strategy of node n that needs A: Choose

backoff time as an exponential variable with mean

Need to know the parameters of all other nodes!

A Distributed Mechanism

• I don’t know other nodes’ strategies• I can estimate them by monitoring

system history• Update my strategy accordingly

Theorem• The mechanism converges to a Nash

Equilibrium• Node’s cost decreases with each update

Simulation Setup

• 10 nodes need A, and 10 nodes need B• gn=1, wn uniformly distributed between [1,2]

• Each node decides its initial policy by assuming that there are 100 nodes that need the same file as it does, and all nodes have the same value of wn as itself

• Each node uses the mechanism to update its strategy

Simulation Results

Performance Analysis

• Only two files in the system• Strategy of node n that needs A: Choose

backoff time as an exponential variable with mean

• Theorem: This strategy is a Nash Equilibrium• Average amount of time on backoff:

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C

backoff backoff

backoff backoff

backoffbackoff

B C

B C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C

Time up backoff

backoff backoff

backoffbackoff

B C

B C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B C

Have: A, B Need: C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

Cbackoff backoff

backoffbackoff

B C

B C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

Cbackoff backoff

backoffTime up

B C

B C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B CC

C C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B C

A B+

C C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B C

A B+B = A-

B

B

C C

Incorporating Network Coding for Multiple Files

AA BB

A

A

C

C B C

B C

A B+ B = A-

B

B

C C

A

A

Performance Analysis for Network Coding

• Theorem: When there are multiple files with network coding employed, and every node chooses its backoff time as an exponential random variable, the Nash Equilibrium can be computed by solving a series of linear equations

Conclusions

• We propose a non-monetary protocol to address the free rider problem in wireless P2P networks

• The core idea is to apply random backoff• Derive closed-form Nash Equilibrium• Propose a distributed mechanism for

convergence• Extend the protocol to incorporate network

coding