Yang, Wenjun2024-09-232024-09-232024https://hdl.handle.net/1828/20447Emerging applications demand stringent quality of services (QoS). Meanwhile, future 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 transport layer protocol design in mobile networks. First, multipath transfer is promising in tackling mobility issues for a 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), ensuring that the reordering delay of each packet is minimized in various mobility scenarios and thus the QoS is significantly improved. Enabling multipath transfer in the Integrated Terrestrial and LEO Satellite Network (ITSN) is promising. However, the existing multipath congestion control algorithms in ITSN suffer from bandwidth under-utilization or overshooting issues due to the high-speed network movement. 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 multipath transfer. Assuming the QoS requirements of the application are known by the 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, 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.enQoSmobilitymultipath transferThesis[121] Amir Sepahi, Lin Cai, Wenjun Yang, and Jianping Pan. Meta-DAMS: Delay-aware multipath scheduler using hybrid meta reinforcement learning. In 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), pages 1–5, 2023.[124] Shengjie Shu, Wenjun Yang, Jianping Pan, and Lin Cai. A multipath extension to the QUIC module for ns-3. In Proceedings of the 2023 Workshop on ns-3, pages 86–93, 2023.[160] Wenjun Yang, Lin Cai, Shengjie Shu, and Jianping Pan. Scheduler design for mobility-aware multipath QUIC. In GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pages 2849–2854, 2022.[161] Wenjun Yang, Lin Cai, Shengjie Shu, and Jianping Pan. Mobility-aware congestion control for multipath QUIC in integrated terrestrial satellite networks. IEEE Transactions on Mobile Computing, 2024.[163] Wenjun Yang, Lin Cai, Shengjie Shu, Jianping Pan, and Amir Sepahi. MAMS: Mobility-aware multipath scheduler for MPQUIC. IEEE/ACM Transactions on Networking, pages 1–16, 2024.[164] Wenjun Yang, Pingping Dong, Lin Cai, and Wensheng Tang. Loss-aware throughput estimation scheduler for multi-path TCP in heterogeneous wireless networks. IEEE Transactions on Wireless Communications, 20(5):3336–3349, 2021.[166] Wenjun Yang, Shengjie Shu, Lin Cai, and Jianping Pan. MM-QUIC: Mobility-aware multipath QUIC for satellite networks. In 17th International Conference on Mobility, Sensing and Networking (MSN), pages 608–615. IEEE, 2021.