DOUBLE SARSA BASED MACHINE LEARNING TO IMPROVE QUALITY OF VIDEO STREAMING OVER HTTP THROUGH WIRELESS NETWORKS

The adaptive streaming over HTTP is widely advocated to enhance the Quality of Experience (QoE) in a bitrate constrained IP network. However, most previous approaches based on estimation of available link bandwidth or fullness of media buffer tend to become ineffective due to the variability of IP traffic patterns. In this paper, we propose a Double State-Action-Reward-State-Action (Sarsa) based machine learning method to improve user QoE in IP network. The Pv video quality estimation model specified in ITU-T P.1203.1 recommendation is embedded in the learning process for the estimation of QoE. We have implemented the proposed Double Sarsa based adaptation method on the top of HTTP in a 4G wireless network and assessed the resulting quality improvement by using full reference video quality metrics. The results show that the proposed method outperforms an existing approach and can be recommended in standardization of future audio-visual streaming services over wireless IP network. We observed the average improvement of 7% in PSNR and 25% in VQM during the live streaming of video.