Favor: fine-grained video rate adaptation

PubDate: June 2018

Teams: The University of Texas at Austin，Network Technology Lab

Writers: Jian He；Mubashir Adnan Qureshi；Lili Qiu；Jin Li；Feng Li；Lei Han

PDF: Favor: fine-grained video rate adaptation

Abstract

Video rate adaptation has large impact on quality of experience (QoE). However, existing video rate adaptation is rather limited due to a small number of rate choices, which results in (i) under-selection, (ii) rate fluctuation, and (iii) frequent rebuffering. Moreover, selecting a single video rate for a 360° video can be even more limiting, since not all portions of a video frame are equally important. To address these limitations, we identify new dimensions to adapt user QoE – dropping video frames, slowing down video play rate, and adapting different portions in 360° videos. These new dimensions along with rate adaptation give us a more fine-grained adaptation and significantly improve user QoE. We further develop a simple yet effective learning strategy to automatically adapt the buffer reservation to avoid performance degradation beyond optimization horizon. We implement our approach Favor in VLC, a well known open source media player, and demonstrate that Favor on average out-performs Model Predictive Control (MPC), rate-based, and buffer-based adaptation for regular videos by 24%, 36%, and 41%, respectively, and 2X for 360° videos.