Flow Level QoE of Video Streaming in Wireless Networks

TL;DR

This paper develops a framework to analyze how flow dynamics affect video streaming QoE in wireless networks, focusing on prefetching delays and buffer starvations, considering both CBR and VBR streaming.

Contribution

It introduces a novel analytical framework using PDEs and ODEs to quantify QoE impacts of flow dynamics, including opportunistic scheduling and variable bitrate streaming.

Findings

Flow dynamics are the main cause of playback starvation.

Average throughput and playback rate primarily determine QoE.

Variances in throughput and playback rate have limited impact on starvation.

Abstract

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.