Degradation Detection of Video Streaming on Mobile and Fixed Networks Using Digital Twins
Lead Research Organisation:
Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science
Abstract
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)
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)
People |
ORCID iD |
| John Schormans (Primary Supervisor) | |
| Yemisi Oyeleke (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/V519935/1 | 01/10/2020 | 30/04/2028 | |||
| 2604130 | Studentship | EP/V519935/1 | 01/10/2021 | 30/09/2025 | Yemisi Oyeleke |