Quality of experience assessment of rate adaptation algorithms in DASH: An experimental study

Abstract

With the widespread use of dynamic adaptive streaming over HTTP (DASH) for online video streaming, ensuring the user's quality of experience (QoE) is of importance to both service and network providers to improve their revenue. DASH aims to adapt the bitrate based on the available bandwidth, while minimizing the number of playback interruptions. This is typically achieved with a rate adaptation algorithm, that chooses an appropriate representation for the next video segment. Most of the algorithms use buffer occupancy, measured throughput, or a combination of these to decide the best representation for next segment. In this paper, we investigate the influence of rate adaptation algorithms on the QoE metrics. We implement five different rate adaptation algorithms and experimentally evaluate them under varying bandwidth and network scenarios. We use objective metrics such as, playback start time, average bitrate played, number of bitrate switching events, number of interruptions and duration of the interruptions to assess the QoE. Our results demonstrate that algorithms that consider both throughput and buffer occupancy results in better QoE. Further, we observe that algorithms considering segment size remove the interruptions alongside improving average bitrate played. We observe that due to the mutual dependency of QoE metrics, most of the algorithms do not necessarily improve QoE while selecting the best bitrate.