Rate Adaptation and Admission Control for Video Transmission With Subjective Quality Constraints

TLDR: In this paper, the authors proposed a rate adaptation algorithm that can incorporate QoE constraints on the empirical cumulative distribution function (eCDF) of the predicted video quality, which can reduce the risk of playback interruptions caused by channel throughput fluctuations.

Abstract: Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by 40% over conventional average-quality maximized rate-adaptation algorithms.