CNS Core: Small: VIQI: Video Quality of Experience Inference Using Network Measurements

消费视频在互联网流量中占据主导地位，而且没有减弱的迹象。除了作为一种受欢迎的娱乐来源外，互联网视频及其相关的体验质量（QoE）直接影响着公民与社会的参与，从向公民通报时事和问题的新闻到提供经济机会的终身技能发展。内容提供商可以监控终端用户在其应用程序上的QoE，并使用这些测量来优化流媒体服务设计。然而，对于互联网服务提供商（isp）来说，获取终端用户视频QoE信息是一项挑战，因为他们无法访问用户设备、设备本身或服务器上的流媒体应用程序。该项目的目标是开发科学方法，使最终用户isp能够使用网络测量来推断视频QoE。通过与两个实际网络运营商的合作，一个移动网络运营商网络和佐治亚理工学院校园网络，该项目将实现跨许多不同视频服务、终端用户设备和网络数据类型的大规模视频QoE推断。大多数视频服务部署HTTP自适应流（HAS）技术，不需要特殊的网络配置或资源预留，而是动态探测带宽可用性，并根据带宽波动调整视频质量。对于此类服务，视频服务质量是通过通常被认为与用户满意度相关的视频QoE指标（如平均带宽和视频摊位数量）来实际有效地估计的。最终用户所依附的ISP寻求对最终用户QoE和ISP性能之间关系的深入理解。这项工作将(1)为来自不同网络类型的各种可用网络测量数据开发QoE推断技术；(2)探索会话建模作为QoE推理方法的使用；(3)开发一种新的机器学习推理方法，该方法由基于会话的建模的见解提供信息；(4)研究可扩展性技术，权衡推理的准确性与现实世界的可行性；(5)制定持续校准和培训的框架。该项目将实现视频QoE的大规模推断，利用和发展工业界和学术界之间的伙伴关系，并将为研究人员和教育工作者提供工具和数据。项目网页为http://www.cc.gatech.edu/~ammar/VIQI.html。佐治亚理工学院Smartech （https://smartech.gatech.edu）将被用作长期存储库。这将通过上面的项目网页链接。目标是在项目结束后至少保留项目数据5年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Consumer video dominates Internet traffic and shows no signs of abating. In addition to being a popular source of entertainment, Internet video and its associated Quality of Experience (QoE) directly affects citizen engagement with society, from news that informs citizens of current events and issues to lifelong skill development that provides economic opportunity. Content providers can monitor end-user QoE on their apps and use these measurements to optimize the streaming service design. It is, however, challenging for Internet Service Providers (ISPs) to obtain end-user video QoE information as they lack access to streaming apps on user devices, the device itself, or the servers. The goal of this project is to develop scientific approaches that enable end-user ISPs to use network measurements to infer video QoE. Through collaboration with two real-world network operators, a Mobile Network Operator network and the Georgia Tech campus network, the project will enable large scale inference of video QoE across many different video services, end-user devices, and network data types. Most video services deploy HTTP Adaptive Streaming (HAS) techniques that require no special network provisioning or resource reservations, and instead dynamically probe bandwidth availability and adjust video quality up or down based on bandwidth fluctuations. For such services, video service quality is practically and usefully estimated through video QoE metrics that are generally believed to correlate with user satisfaction, such as average bandwidth and number of video stalls. ISPs, where the end-user attaches, seek an in-depth understanding of the relationship between end-user QoE and ISP performance. This work will (1) develop QoE inference techniques for a variety of available network measurement data from different network types; (2) explore the use of session modeling as an approach to QoE inference; (3) develop a novel machine learning approach to inference that is informed by insights from session-based modeling; (4) investigate scalability techniques that tradeoff accuracy of inference with real-world feasibility; and (5) develop a framework for continuous calibration and training. This project will enable large scale inference of video QoE, leverage and develop partnerships between industry and academia, and will produce tools and data available to researchers and educators.The project web page is http://www.cc.gatech.edu/~ammar/VIQI.html. Georgia Tech Smartech (https://smartech.gatech.edu) will be used as a long term repository. This will be linked through the project web page above. The goal is to preserve the project data for at least 5 years beyond the end of project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.