5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks
5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks
复制标题
DOI：
10.1109/tbc.2018.2816786
复制
发表时间：
2018-06-01
影响因子：
4.5
通讯作者：
Wang, Qi
中科院分区：
文献类型：
--
作者：
Nightingale, James;Salva-Garcia, Pablo;Wang, Qi
预计未来第五代 (5G) 移动网络的流量将由具有挑战性的视频应用程序主导，例如移动广播、远程手术和增强现实，要求实时和超高质量传输。 5G 网络的两个主要期望是，它们将能够处理超高清 (UHD) 视频流，并通过采用体验质量 (QoE) 感知网络管理方法来提供满足最终用户感知质量要求的服务。本文提出了一个 5G-QoE 框架来解决 5G 网络中超高清视频流的 QoE 建模问题。特别是，它专注于提供一个 QoE 预测模型，该模型既足够准确，又具有足够低的复杂性，可以用作未来 5G 网络所需规模的视频应用程序流“健康状况”的连续实时指标。该模型是作为欧盟 5G PPP SELFNET 自主管理框架的一部分开发和实施的，它提供了穿越现实多租户 5G 移动边缘网络测试台的超高清视频应用程序流可能感知质量的主要指标。所提出的5G-QoE框架已在5G测试台中实施，并且通过将预测的QoE值与主观测试结果以及测试台中的经验测量进行比较，验证了QoE预测的高精度。因此，5G-QoE 将实现整体视频流自我优化系统，采用尖端的可扩展 H.265 视频编码，以 QoE 感知方式传输超高清视频应用程序。
Traffic on future fifth-generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time, and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle ultra-high-definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting quality of experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the "health" of video application flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner.