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.

翻译：质量保证机制使流流平台能够提供被认为足以使用户满意的质量水平,而转移的数据数量最少。已经制定了各种措施和办法,以控制视频质量,例如根据网络条件加以调整,其中包括质量和阈值的客观矩阵表,通过主观的认知判断来确定质量和阈值。前一组矩阵最近得到了(多)媒体研究人员的注意。它们称为“经验质量”研究领域。在本文件中,我们介绍了对QoE理论模型的审查,同时讨论了其特性和对实地的影响。我们说,其中大多数是建模的自下而上的方法。这些模型侧重于尽可能多的变量,但调查它们之间因果关系的能力有限;因此,在实际中,调查结果的可适用性有限。为了推进实地工作,我们因此提议了一个结构上下调的视频QE模型,描述各种变量之间的因果关系。我们希望,我们的框架将便利设计可比较的域实验。