Anticipatory Radio Resource Management for Mobile Video Streaming with Linear Programming

TL;DR

This paper introduces a linear programming-based anticipatory resource management method for mobile video streaming that improves spectral efficiency and reduces stalling by predicting user buffer states and optimizing resource allocation.

Contribution

It presents a novel linear programming approach integrating user buffer models for anticipatory radio resource management in video streaming.

Findings

Significant reduction in stalling duration.

Improved spectral efficiency over instantaneous adaptation.

Robustness against prediction errors.

Abstract

In anticipatory networking, channel prediction is used to improve communication performance. This paper describes a new approach for allocating resources to video streaming traffic while accounting for quality of service. The proposed method is based on integrating a model of the user's local play-out buffer into the radio access network. The linearity of this model allows to formulate a Linear Programming problem that optimizes the trade-off between the allocated resources and the stalling time of the media stream. Our simulation results demonstrate the full power of anticipatory optimization in a simple, yet representative, scenario. Compared to instantaneous adaptation, our anticipatory solution shows impressive gains in spectral efficiency and stalling duration at feasible computation time while being robust against prediction errors.