Score-based objective quality of experience assessment of DASH adaptation algorithms

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Dynamic adaptive streaming over HTTP (DASH) is gaining popularity for video streaming over the Internet. DASH uses a rate adaptation algorithm to adapt the bitrate played continuously based on the network conditions. Most of the algorithms use buffer occupancy, throughput, or a combination thereof to adapt the bitrate. In the literature, the Quality of Experience (QoE) of users is measured objectively using metrics such as, number of interruptions and their duration, average bitrate played, and the number of bitrate switches. In this work, we propose a score-based approach to assess the user experience of different rate adaptation algorithms objectively. We experimented with five algorithms implemented using an HTML5 player under varying bandwidth and network conditions. Our results demonstrate that algorithms considering both measured bandwidth and playback buffer occupancy lead to better QoE even under low bandwidth conditions. We show that buffer-based algorithms do not work well in networks with low bandwidth. Algorithms that only try to improve the bitrate played often end up with more interruptions or bitrate switches when bandwidth fluctuates. Due to the mutual dependency among the objective metrics, most of the algorithms do not necessarily improve overall QoE while selecting appropriate bitrate.