A Survey on Quality of Experience of HTTP Adaptive Streaming
A Survey on Quality of Experience of HTTP Adaptive Streaming
复制标题
DOI：
10.1109/comst.2014.2360940
复制
发表时间：
2015-01-01
影响因子：
35.6
通讯作者：
Phuoc Tran-Gia
中科院分区：
文献类型：
--
作者：
Seufert, Michael;Egger, Sebastian;Phuoc Tran-Gia
不断变化的网络条件给互联网上的视频流带来了严重的问题。HTTP自适应流（HAS）是许多视频服务采用的技术，其通过使视频适应当前网络条件来缓解这些问题。它使服务提供商能够通过整合来自不同层的信息来提高资源利用率和体验质量（QoE），以便以最佳质量交付和调整视频。因此，它允许考虑终端用户设备能力、可用视频质量水平、当前网络条件和当前服务器负载。对于最终用户，与经典HTTP视频流传输相比，HAS的主要好处是减少了视频回放的中断和更高的带宽利用率，这两者通常都会导致更高的QoE。自适应可以通过改变视频的帧速率、分辨率或量化来实现，这可以通过各种自适应策略以及相关的客户端和服务器端操作来完成。HAS的技术发展，现有的开放式标准化解决方案，但也专有的解决方案在本文中作为根本得到的QoE的影响因素，出现的适应。主要的贡献是一个全面的调查QoE相关的工作，从人机交互和网络领域，这是根据QoE的视频适应的影响结构。更准确地说，重新审视了涵盖适应维度和策略的QOE方面的主观研究。结果，识别了HAS的QoE影响因素和相应的QoE模型，但也讨论了开放性问题和相互矛盾的结果。此外，技术的影响因素，这往往被忽视的背景下，HAS，影响感知QoE的影响因素，因此进行了分析。本调查为读者提供了当前技术水平和最新发展的概述。同时，它的目标是为HTTP视频流开发新解决方案或从以用户为中心的角度评估视频流的网络研究人员。因此，本文是朝着真正改善HAS迈出的重要一步。
Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client-and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.