An LSTM-based Approach for Overall Quality Prediction in HTTP Adaptive Streaming

Abstract

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. In HAS, video quality is generally varying in each streaming session. Therefore, a key question in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this paper, we propose a machine learning approach for overall quality prediction in HTTP Adaptive Streaming. In the proposed approach, each segment is represented by four features of segment quality, stalling durations, content characteristics, and padding. The features are fed into a Long Short Term Memory (LSTM) network that is capable of exploring temporal relations between segments. The overall quality of the streaming session is predicted from the outputs of the LSTM network using a linear regression module. Experiment results show that the proposed approach is effective in predicting the overall quality of streaming sessions. Also, it is found that our proposed approach outperforms four existing approaches.