Knowledge-Driven Rendering Task Offloading Strategy for Virtual Reality in MEC-Enabled Wireless Networks

Due to the stringent latency requirements for computationally intensive rendering in virtual reality (VR) transmission and the limitations of computational resources on VR devices, extensive research has focused on task offloading with joint communication and computing resource scheduling to address these issues. Traditional model-based theoretical methods face challenges with long online processing times, while data-driven methods lack interpretability. This paper proposes a knowledge-driven rendering task offloading strategy for immersive wireless VR with mobile edge computing (MEC). The rendering approaches include local, MEC, and collaborative offloading between VR devices and MEC servers. First, we formulate an optimization problem to maximize user quality of experience (QoE), which is defined as the weighted sum of latency and video resolution. To solve the optimization problem, we propose a knowledge-driven belief propagation (KD-BP) algorithm where the structure of the BP algorithm is regarded as knowledge. Specifically, the operations with high computational complexity in the BP algorithm are replaced by a deep neural network, termed the knowledge-fused deep learning (DL) method. Finally, numerical results show that when the number of users reaches 10, the proposed KD-BP algorithm significantly reduces online processing latency and closely matches the convergence speed and performance compared to the BP algorithm.