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

CNS 核心：小型：VIQI：使用网络测量进行视频体验质量推断

• 批准号: 1909040
• 负责人: Mostafa Ammar
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909040&HistoricalAwards=false
• 关键词: CNS; Core; Small; VIQI; Video

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.

消费者视频主导着互联网流量，而且没有减弱的迹象。除了作为一种流行的娱乐来源，互联网视频及其相关的体验质量（QoE）直接影响公民与社会的互动，从向公民通报当前事件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其应用上的最终用户QoE，并使用这些测量结果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，获得最终用户视频QoE信息是具有挑战性的，因为他们无法访问用户设备上的流媒体应用、设备本身或服务器。该项目的目标是开发科学的方法，使最终用户ISP能够使用网络测量来推断视频QoE。通过与两家真实网络运营商（一家移动的网络运营商网络和格鲁吉亚理工学院校园网络）的合作，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视频QoE大规模推断。大多数视频服务部署HTTP自适应流（HAS）技术，不需要特殊的网络供应或资源预留，而是动态地探测带宽可用性，并根据带宽波动向上或向下调整视频质量。 对于这样的服务，视频服务质量实际上和有用地通过视频QoE度量来估计，所述视频QoE度量通常被认为与用户满意度相关，诸如平均带宽和视频停顿的数量。最终用户所在的ISP寻求深入了解最终用户QoE与ISP性能之间的关系。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）探索使用会话建模作为QoE推断的方法;（3）开发一种新的机器学习方法来推断，该方法由基于会话的建模的见解所告知;（4）研究权衡推断准确性与现实世界可行性的可扩展性技术;及（5）发展持续校正及培训的架构。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合作伙伴关系，并将为研究人员和教育工作者提供工具和数据。该项目的网页是http://www.cc.gatech.edu/~ammar/VIQI.html。格鲁吉亚科技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。该奖项反映了NSF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。

项目成果

Context-driven Encrypted Multimedia Traffic Classification on Mobile Devices

移动设备上的上下文驱动加密多媒体流量分类

• DOI: 10.1109/percom53586.2022.9762389
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Pervasive Computing and Communications (PerCom
• 作者: Hoque, Mohammad A.;Finley, Benjamin;Rao, Ashwin;Kumar, Abhishek;Hui, Pan;Ammar, Mostafa;Tarkoma, Sasu
• 通讯作者: Tarkoma, Sasu

Drop the packets: using coarse-grained data to detect video performance issues

丢弃数据包：使用粗粒度数据检测视频性能问题

• DOI: 10.1145/3386367.3431294
• 发表时间: 2020
• 期刊: Proceedings of ACM Conext
• 作者: Mangla, Tarun;Halepovic, Emir;Zegura, Ellen;Ammar, Mostafa
• 通讯作者: Ammar, Mostafa