Fair and Efficient Bandwidth Allocation for Video Flows Using Sigmoidal Programming

Abstract

The problem of bandwidth allocation in networks is traditionally solved using distributed rate allocation algorithms under the general framework of Network Utility Maximization(NUM). Despite many advances in solving the flow assignment problem in NUM, the common but unrealistic assumption of concavity of utility functions undermines the performance of existing systems in providing satisfactory QoE to the consumers of video traffic, the utility function of which is not concave, but sigmoidal. In this work, we model the bandwidth allocation problem as a nonconvex Sigmoidal Programming optimization problem and use an approximation algorithm to solve it while guaranteeing a suboptimal solution. Our simulation results for video streaming over two realistic network topologies indicate improvements of at least 50% in average utility, up to 29% in fairness, and on average 14% less network capacity usage, all compared to two existing representative methods: Proportional Fair and Max-Min Fair.