Adaptive Scalable Video Transmission Strategy in Energy Harvesting Communication System

摘要

In this paper, we consider the adaptive transmission problem of scalable video in an energy harvesting communication system. The stochastic nature of the harvested energy puts a new challenge on the video transmission. Against this challenge, we formulate the adaptive scalable video transmission problem as maximizing the time average quality of the transmitted video subject to the energy constraint for reducing the playback interruption and the video quality smoothness constraint. In order to solve this problem, the Lyapunov optimization method is applied to derive an online dynamic layer transmission algorithm (DLTA). The simulation results show that the proposed DLTA can achieve better performance in terms of the received video quality and the convergence rate than a conventional reinforcement learning algorithm like the Q-learning method. It is also illustrated that the energy and smoothness constraints are beneficial for controlling the behavior of DLTA.