Game‐theoretic auction design for bandwidth sharing in helper‐assisted P2P streaming

Abstract

SUMMARYVideo traffic offloading through the helper peers can significantly alleviate the server workload and enhance users' quality of experience. In this paper, the complex interactions of a number of helpers seeking to trade their bandwidth surplus in a peer-to-peer market are studied using non-cooperative game theory and double auction. In particular, a non-cooperative game is formulated among the helpers in which each helper can make a decision on the maximum amount of upload bandwidth surplus that it is willing to sell so as to maximize its utility function that reflects the tradeoff between the benefits from bandwidth sharing and its associated costs. Then, the price governing the bandwidth trading market between the helpers and peers is determined via a truthful double auction mechanism. To solve the game, an algorithm based on the best response dynamics is proposed using which helpers can reach a Nash equilibrium point. Simulation results exhibit that the proposed algorithm achieves significant performance improvements in terms of utility per selling helpers.

Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation

“…( 2017 ) investigated a reputation-based resource allocation mechanism through two non-cooperative games in P2P networks and proved evolutionary stability of reputation-based resource allocation (Goswami et al. 2018 ).…”

“…Rohmer et al (2015) investigated the problem of maximizing the P2P streaming system capacity by effectively alternating between different resource allocation strategies, and combined different, even potentially conflicting, performance objectives when deciding which resource allocation strategy to use for the current time period. Mostafavi and Dehghan (2016) considered bandwidth sharing and allocation in helper-assisted P2P streaming using non-cooperative game theory and double auction, and proposed an algorithm to reach significant performance improvements in terms of utility per selling helpers. They Mostafavi and Dehghan (2017) also considered resource allocation for HD live streaming and proposed a decentralized, stochastic approximation helper selection mechanism which is adaptable to supply and demand pattern of various video channels.…”

Knowledge Engineering on Internet of Things through Reinforcement Learning

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A QoS-Assured and Mobility-Aware Routing Protocol for MANETs

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