QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Abstract

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.