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

These authors present a few critical features for ACMPTC to enhance applications require high bandwidth, ultra-low latency, and its performance--mainly choosing paths with low latency and consistent quality of service (QoS) to deliver seamless, immersive packet loss. It brings a DRL-based agent that can adapt its experiences [2]. Traditional network protocols like the decision to real-time network states and compute dynamic, Transmission Control Protocol (TCP) often struggle to meet optimal choices. This feedback loop, on the other hand, these stringent demands, especially in highly dynamic and allows for real-time path selection and resource allocation that diverse network environments due to single path transmission, enables continuous optimization to provide a smooth AR/VR inadequate for high-bandwidth, low-latency requirement, high experience even with varying network conditions. It confirms latency sensitivity, etc. [3]. These limitations make TCP less that the system operates correctly and provides a way to update effective for dynamic, heterogeneous network environments such a network when there is variation in traffic levels by and the demanding performance needs of modern applications adjusting it effectively.