Adaptive Context-Aware Multi-Path Transmission Control for VR/AR Content: A Deep Reinforcement Learning Approach

TL;DR

This paper presents ACMPTCP, a deep reinforcement learning-based protocol that enhances multi-path data transmission for AR/VR streaming by optimizing path management and bandwidth allocation across various networks.

Contribution

It introduces a novel DRL-based adaptive protocol that improves MPTCP performance specifically for AR/VR content delivery, addressing existing limitations.

Findings

Enhanced network throughput for AR/VR streaming

Improved path management efficiency

Better bandwidth utilization across networks

Abstract

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.