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Faculty of Graduate Studies

Wenjun Yang

Notice of the Final Oral Examination for the Degree of Doctor of Philosophy

Topic

QoE-Oriented Multipath QUIC Protocol Design in 6G Mobile Networks

Department of Electrical Engineering

Date & location

  • Thursday, September 12, 2024

  • 10:00 A.M.

  • Engineering Office Wing

  • Room 430

Reviewers

Supervisory Committee

  • Dr. Lin Cai, Department of Electrical and Computer Engineering, University of Victoria (Supervisor)

  • Dr. Amirali Baniasadi, Department of Electrical and Computer Engineering, UVic (Member)

  • Dr. Kui Wu, Department of Computer Science, UVic (Outside Member) 

External Examiner

  • Dr. Mohamed Hefeeda, Department of Computer Science, Simon Fraser University 

Chair of Oral Examination

  • Dr. Caterina Valeo, Department of Mechanical Engineering, UVic

     

Abstract

Emerging 6G applications demand stringent quality of services (QoS). Meanwhile, 6G networks are featured by ubiquitous mobility. How to meet users’ QoS requirements in highly mobile environments remains an open issue, which motivates our research on QoS-oriented multipath QUIC (MPQUIC) protocol design in 6G networks.

First, MPQUIC is promising in tackling mobility issues for seamless handoff. Scheduling packets across multiple paths, however, has the issue of out-of-order (OFO) arrival due to the heterogeneity of the paths. In this regard, we put forward a Mobility-Aware Multipath Scheduler (MAMS) for MPQUIC. With the knowledge of link variations, MAMS applies a probabilistic model to estimate the expected throughput of each path and allocates a certain amount of packets accordingly to multiple paths, ensuring that the reordering delay of each packet is minimized in various mobility scenarios.

Considering the Integrated Terrestrial and LEO Satellite Network (ITSN) in 6G, we apply MMQUIC to ITSN. Since ITSN is characterized by high bandwidth-delay product (BDP) and high-speed network movement, the existing congestion control algorithms would suffer from bandwidth under-utilization and overshooting issues. Therefore, a novel Mobility-Aware Congestion control (MACO) algorithm is developed.

As applications are the driving force for protocol design, we investigate the performance of video streaming applications using MPQUIC. Assuming the QoS requirements of the application are known by the MPQUIC sender, we adopt a lightweight learning framework, a contextual multi-armed bandit (CMAB), to discover the underlying relationship between dynamic network states and QoS performance, which can intelligently select access networks and adapt FEC coding to trade off delay, reliability, and throughput.

Furthermore, real-time 360-degree videos are not only bandwidth-intensive but also highly sensitive to delays. Ensuring both high video quality and smooth playback experience remains a critical issue. Therefore, we introduce a QoE-oriented Deadline driven (RIDE) algorithm for multipath scheduling at the frame level. RIDE employs a dependency tree to understand deadlines for different types of frames and considers the negative impacts of Field of View (FoV) changes on scheduling decisions. Utilizing an actor-critic framework to train the neural network enables the scheduler agent to adapt to dynamic environments, including network and FoV dynamics.

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