QUTY: Towards Better Understanding and Optimization of Short Video Quality

Abstract

Short video applications such as TikTok and Instagram have attracted tremendous attention recently. However, it is very limited for industry and academia to understand the user's Quality of Experience (QoE) on short video, let alone how to improve the QoE in short video streaming. In this paper, we dug into the factors that affect the user's QoE and then propose a system which models and optimizes user's QoE. We unveil the QoE formulation of short video by diving into the understanding of users' viewing behavior, and analyzing large dataset (more than 10 million records) from Douyin (a short video application). We find that: (a) the increase of rebuffering duration, rebuffering times, and starting delay will decrease the user retention ratio, whereas the video bitrate has little effect, (b) the users exhibit different viewing behavior patterns such as scrolling video fastly or slowly, which can be utilize to improve QoE. Over these findings, we propose QUTY, a QoE-driven short video streaming system, which utilizes a data-driven approach to quantify QoE of short video and optimizes it with a Hierarchical Reinforcement Learning (HRL) method. Our evaluations show that QUTY can reduce the rebuffering ratio by up to 49.9%, reduce the rebuffering times by up to 55.8%, reduce the startup delay by up to 81.9%, and improve the QoE by up to 8.5% compared with the existing short video streaming approaches.