QoE-Aware Bandwidth Allocation for Video Traffic 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 computationally intensive flow assignment problem in NUM, the common but unrealistic assumption of concavity of utility functions undermines the performance of existing systems in providing satisfactory quality of experience (QoE) to consumers of video traffic, the utility function of which is not concave, but sigmoidal. The authors propose to model the bandwidth allocation problem as a sigmoidal programming problem, more closely representing video traffic, and solve this nonconvex optimization problem using an approximation algorithm. Their simulation results for video streaming over a range of tree-shaped content delivery networks indicate improvements of at least 60 percent in average utility/QoE and 45 percent in fairness, while using slightly fewer network resources, compared to two representative methods: proportional fair and max-min fair.