Adaptive Context-Aware Multi-Path Transmission Control for VR/AR Content: A Deep Reinforcement Learning Approach
This addresses network optimization for AR/VR content delivery, representing an incremental improvement over conventional MPTCP.
The paper tackled the problem of optimizing Multi-Path Transmission Control Protocol (MPTCP) for AR/VR streaming by introducing ACMPTCP, which uses deep reinforcement learning for path management and bandwidth allocation, resulting in improved performance for data-intensive applications.
This paper introduces the Adaptive Context-Aware Multi-Path Transmission Control Protocol (ACMPTCP), an efficient approach designed to optimize the performance of Multi-Path Transmission Control Protocol (MPTCP) for data-intensive applications such as augmented and virtual reality (AR/VR) streaming. ACMPTCP addresses the limitations of conventional MPTCP by leveraging deep reinforcement learning (DRL) for agile end-to-end path management and optimal bandwidth allocation, facilitating path realignment across diverse network environments.