  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.
