## 1 Introduction
### Related Work  
From the perspective of an individual VR user, although user behavior can change arbitrarily over time, studies show that head movement in VR can be predicted with high accuracy within a few hundred milliseconds [8]. Therefore, FoV prediction can be used to pre-fetch the required content for the user, reducing the operational load on data transmission and image processing imposed by real-time application demands [4]. To enhance the quality of personal experience, in [5], a decentralized and personalized federated learning (DP-FL)-based caching strategy is proposed to predict the FoV of each VR user in a multi-base station (BS) wireless network. To meet the demand for low latency and high bandwidth of VR, in [9], the authors propose a cloud-edge collaborative caching strategy, where the background content is pre-cached at the edge nodes and interactive content is generated by an AI-aided cloud-edge collaborative system. To minimize the system's long-term energy expenditure, in [10], the authors propose a cross-layer joint design scheme that integrates multi-MEC server collaborative video caching, multi-point cooperative transmission between BSs, and beamforming with reconfigurable intelligent surfaces. However, the aforementioned studies on caching strategy design for VR service focus more on improving cache hit ratio under storage constraint, thus neglecting the impact of time-varying transmission rate on either the wired network from the content server to the MEC server or the wireless network from the MEC-connected BS to the VR users. In the literature, jointly designing proactive caching with computing offloading is an important approach to enhancing VR video services. Aiming to maximize the average tolerable delay, in [11], the authors investigate the optimal caching and computing offloading decision scheme in a single-user, single-MEC server scenario. The problem is modeled as a multi-choice multidimensional knapsack problem, and the optimal decision is obtained after transforming it into a Lagrangian relaxation problem. Similarly, in [12], the optimal caching and computing offloading decisions are studied for a single-user, single-MEC server setting, to minimize the average transmission rate of VR videos. For VR videos stored in the cloud server, an MEC-based 360\({}^{\circ}\) mobile VR video streaming scheme with FoV prediction is proposed in [13], which jointly considers video coding, proactive caching, computing offloading, and data transmission. Considering that the cached video still requires rendering through the MEC server's computing offloading, in [14], content caching and computing offloading for rendering are jointly designed with a rendering-aware multi-MEC server collaborative caching optimization method. For multi-user VR scenarios, in [15], the authors study a similar problem to that in [14]. To address the challenges of QoS provisioning of VR users under partial observation in decentralized networks, in [16], the authors propose a distributed computing framework based on multi-agent deep deterministic policy gradient (MADDPG) for joint optimizing terminal-cooperative caching and offloading for VR tasks. Although the aforementioned works propose various efficient solutions to jointly utilize proactive caching and computing offloading for VR, most of them overlook the issue of how VR video data are transmitted from the data source (e.g., the cloud VR server) to the network edge, again neglecting the impact of time-varying transmission rate on the end-to-end (E2E) transmission path. On the other hand, there is no work proposing multiuser access control algorithms tailored to VR systems by exploiting both proactive caching and computing offloading, while taking into account the real-time and dynamic network resources.