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This paper presents a Brownian-approximation framework to optimize thequality of experience (QoE) for real-time video streaming in wireless networks.In real-time video streaming, one major challenge is to tackle the naturaltension between the two most critical QoE metrics: playback latency and videointerruption. To study this trade-off, we first propose an analytical modelthat precisely captures all aspects of the playback process of a real-timevideo stream, including playback latency, video interruptions, and packetdropping. Built on this model, we show that the playback process of a real-timevideo can be approximated by a two-sided reflected Brownian motion. Throughsuch Brownian approximation, we are able to study the fundamental limits of thetwo QoE metrics and characterize a necessary and sufficient condition for a setof QoE performance requirements to be feasible. We propose a scheduling policythat satisfies any feasible set of QoE performance requirements and then obtainsimple rules on the trade-off between playback latency and the video interruptrates, in both heavy-traffic and under-loaded regimes. Finally, simulationresults verify the accuracy of the proposed approximation and show that theproposed policy outperforms other popular baseline policies.

Fresher Content or Smoother Playback? A Brownian-Approximation Framework for Scheduling Real-Time Wireless Video Streams