在通信生态系统中，用户使用视频业务的体验质量（QoE）受到系统多方面因素的复杂影响，哪些因素是影响QoE的主要因素，如何利用这些影响因素及其他信息对QoE进行评估，这些问题均尚不明确。本课题针对现有视频业务的主流：基于HTTP的动态自适应视频流（DASH）业务，研究通信生态系统中的QoE评估方法。首先分析通信生态系统中DASH视频业务影响用户QoE的主要因素，构建基于子体验质量的分层QoE评估框架。然后针对两项重要的子体验质量，分别研究基于感知累积效应的视频感知质量评估模型，和基于用户行为的内容感兴趣程度评估模型。进而将子体验质量进行联合，建立考虑用户感兴趣程度的QoE评估模型。课题旨在为DASH视频业务的QoE评估提供理论基础和系统的解决方法。相关研究成果预计能够为研究各类网络视频业务QoE的感知机制提供科学依据和方法，并为保障相关应用的体验质量提供解决方案，具有重要的理论和实际意义。

In the communication ecosystem, Quality of Experience (QoE) of video services is influenced by various complicated factors. It is however not clear what are the major influential factors to QoE and how to use those factors as well as other information to evaluate QoE. Targeting at the state-of-the-art of video services, i.e., Dynamic Adaptive Streaming over HTTP (DASH), this project works on its QoE assessment in communication ecosystem. The major influential factors to QoE with respect to DASH video services will be first analyzed, based on which a layered framework for QoE assessment using sub-QoE will be established. Then two fundamental sub-QoE will be addressed, and the corresponding models will be built. That is, the sub-QoE model for perceptual quality for videos based on the accumulation effect in perception, and the sub-QoE model for users’ interests in video content using parameters for behavior features. Furthermore, the two sub-QoE will be pooled to generate the objective QoE assessment model considering users’ interests. This project is dedicated to the theoretical foundation and systematic solution for QoE assessment for DASH video services. The corresponding outcome is expected to provide scientific basis and methodology for research on the perception mechanism of QoE for various networked video services, and technical solutions for the guarantee of QoE in related applications.