Anticipatory Radio Resource Management for Mobile Video Streaming with Linear Programming
This work addresses quality of service issues for mobile video streaming users by improving resource management, though it is incremental as it builds on existing anticipatory networking concepts.
The paper tackles the problem of allocating radio resources for mobile video streaming by integrating a user's play-out buffer model into the network, formulating it as a Linear Programming problem to optimize resource allocation and reduce stalling time. Simulation results show impressive gains in spectral efficiency and stalling duration compared to instantaneous adaptation, with feasible computation time and robustness against prediction errors.
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.