Top-down and bottom-up approaches to video Quality of Experience studies; overview and proposal of a new model

TL;DR

This paper reviews existing QoE models for video streaming, critiques their limitations, and proposes a new top-down causal model to improve understanding and experimental design in video quality assessment.

Contribution

It introduces a novel top-down structural model of video QoE, addressing the limitations of existing bottom-up approaches by focusing on causal relationships among variables.

Findings

Existing models mainly adopt a bottom-up approach.

The proposed model emphasizes causal relationships.

The framework aims to improve experimental consistency.

Abstract

Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi)media researchers. They call this area of study ``Quality of Experience'' (QoE). In this paper, we present a review of QoE's theoretical models together with a discussion of their properties and implications for the field. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them; therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. We hope that our framework will facilitate designing comparable experiments in the domain.