Adaptive Context-Aware Multi-Path Transmission Control for VR/AR Content: A Deep Reinforcement Learning Approach
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 0 of its 23 references that are in the map. First posted on arXiv 27 Dec 2024.
Builds on
0 of 23 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Multi-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks
- 2025ASL360: AI-Enabled Adaptive Streaming of Layered 360^circ Video over UAV-assisted Wireless Networks
- 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
- 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
- 2024Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
- 2025Neural-Enhanced Rate Adaptation and Computation Distribution for Emerging mmWave Multi-User 3D Video Streaming Systems
- 2025The Emergence of Deep Reinforcement Learning for Path Planning
- 2026Training-Free Adaptive 360-degree Video Streaming via Semantic Potential Fields