Degradation Detection of Video Streaming on Mobile and Fixed Networks Using Digital Twins

使用数字孪生对移动和固定网络上的视频流进行退化检测

• 批准号: 2604130
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2604130
• 关键词: Degradation; Detection; Video; Streaming; Mobile

There has been tremendous success with predicting quality of experience (QoE) of video streaming using application and network level metrics. Most of these have been done by simulating video streaming session over different network conditions and recording the video's quality or viewers' perception of the streamed video.This paper explores the use of Data-driven Performance Digital Twin (DT) which will be built as part of this research, as a platform on which degradation detection of video streaming over QUIC and TCP protocols can be carried out using AI/ML (Artificial Intelligence/Machine Learning) and suggestion of what caused the degradation so that network operators can use them to optimize their network. YouTube videos will be used in this research.(Abstract taken from Year 1 Progression Report, August 2022. It will be updated by the end of 2023)

使用应用和网络级别的指标预测视频流的体验质量(QOE)已取得巨大成功。其中大多数是通过模拟不同网络条件下的视频流会话并记录视频质量或观众对流视频的感知来完成的。本文探讨了数据驱动性能数字孪生(DT)的使用，作为本研究的一部分，作为平台的一部分，可以使用AI/ML(人工智能/机器学习)来检测QUIC和TCP协议上的视频流的降级，并建议造成降级的原因，以便网络运营商可以利用它们来优化他们的网络。这项研究将使用YouTube视频。(摘自2022年8月的第一年进展报告。它将在2023年底之前更新)