A Study on Impacts of Multiple Factors on Video Qualify of Experience

TL;DR

This paper quantitatively analyzes how perceptual quality and interruptions jointly influence the quality of experience in HTTP Adaptive Streaming, providing insights to enhance streaming QoE.

Contribution

It introduces a novel histogram-based model to quantify the relative impacts of quality and interruptions on streaming QoE using large datasets.

Findings

Perceptual quality and interruptions significantly affect QoE.

Histogram modeling reveals the relative importance of factors.

Insights suggest targeted improvements for streaming services.

Abstract

HTTP Adaptive Streaming (HAS) has become a cost-effective means for multimedia delivery nowadays. However, how the quality of experience (QoE) is jointly affected by 1) varying perceptual quality and 2) interruptions is not well-understood. In this paper, we present the first attempt to quantitatively quantify the relative impacts of these factors on the QoE of streaming sessions. To achieve this purpose, we first model the impacts of the factors using histograms, which represent the frequency distributions of the individual factors in a session. By using a large dataset, various insights into the relative impacts of these factors are then provided, serving as suggestions to improve the QoE of streaming sessions.