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 AB A,B

Wireless P2P Exchange data locally instead of getting all packets from the base station AB A,B AB

Wireless P2P Exchange data locally instead of getting all packets from the base station AB A,B ABAB

Wireless P2P Exchange data locally instead of getting all packets from the base station AB A B AB

Wireless P2P Exchange data locally instead of getting all packets from the base station AB A 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 B Need: 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 B Need: B Need: A A B

Free Rider Problem The broadcast wireless channels make it a little bit more tricky… A A B B Need: B Need: A …….

Free Rider Problem A A B B A B The broadcast wireless channels make it a little bit more tricky…

Free Rider Problem A A B B A 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: g n {avg. transmissions per download} +w n {avg. inter-download delay} g n : price for transmission w n : price for waiting

Protocol Illustration A A B B Time

Protocol Illustration A A B B backoff Time

Protocol Illustration A A B B Time up backoff Time Have: A Need: B

Protocol Illustration A A B B backoff Time

Protocol Illustration A A B B backoff Time up Time B

Protocol Illustration A A B B Time A B B

Protocol Illustration A A B B Time 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 g n =1, w n 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 w n 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 A A B B A A C C B C B C

A A B B A A C C backoff B C B C

Incorporating Network Coding for Multiple Files A A B B A A C C Time up backoff B C B C

Incorporating Network Coding for Multiple Files A A B B A A C C B C B C Have: A, B Need: C

Incorporating Network Coding for Multiple Files A A B B A A C C backoff B C B C

Incorporating Network Coding for Multiple Files A A B B A A C C backoff Time up B C B C

Incorporating Network Coding for Multiple Files A A B B A A C C B C B C C C C

A A B B A A C C B C B C A B + C C

A A B B A A C C B C B C A B + B = A - B B C C

A A B B 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