Probabilistic Constrained Optimization for Predictive Video Streaming by Deep Learning

This paper optimizes predictive power allocation to minimize the average transmit power for video streaming subject to the constraint on stalling time, one of the most important factors affecting the experience of users requesting video-on-demand service. Different from the widely used first-predict-then-optimize strategy that regards the predicted channels as the real future channels, we integrate the prediction of large-scale channels into the optimization of power allocation, such that the quality of service constraint can be controlled. Due to the channel prediction errors, stalling is unavoidable and the stalling duration is random. This motivates us to consider an average stalling fraction constraint conditioned on the observed large-scale channel gains, which can be transformed into a conditional probabilistic constraint. The resultant optimization problem is difficult to solve since the probabilistic constraint lacks a closed-form expression. We resort to end-to-end deep learning to optimize the future powers from the past channels. In particular, we propose a method to learn the conditional probabilities in multiple steps with a single neural network. Simulation results show the advantages of the proposed method in reducing average power consumption and in ensuring the probabilistic constraint.