## 1 Introduction  
In recent years, with the rapid proliferation of wireless IoT applications such as connected vehicles, VR/AR, smart cities, and personalized streaming videos, there has been a surge in computation-sensitive and real-time services in mobile networks [1]. These services typically require completion within a short time frame (20-125 ms) [2]. Figure 1 illustrates the workflow of real-time services. Generally, three steps are involved: generation, processing, and transmission [3]. Firstly, mobile users (MUs) such as connected vehicles, AR/VR devices, mobile phones, or drones would continuously generate sensor or user data (infrared, radar, video streams, health, and so on) when requesting specific real-time services. Subsequently, MUs can process these tasks locally or transmit them to server units located in the cloud or edge to obtain computing services. The computing operations will be carried out with the assistance of application data (executable programs). The computing results return to the MUs at the end of the time frame. For example, vehicles requiring autonomous driving services would detect environment data from fusion monitoring devices (in-vehicle cameras, millimeter-wave or ultrasonic radar, etc.) while driving. After preprocessing, these raw data are sent to computing units deployed with corresponding target detection algorithms (or processed directly by locally embedded computing units) to perceive road conditions and provide optimal driving decisions. This necessitates an efficient, cost-effective service model that minimizes user energy consumption while ensuring service quality. Traditional cloud computing architectures, as a centralized solution, encounter challenges such as severe wide-area network latency and fluctuating service quality when handling these new types of services. Multi-access edge computing (MEC) [4] emerges as a computing paradigm capable of addressing these issues. It leverages the distributed computing power and communication resources at the network edge, particularly at the edge of the mobile wireless access network (RAN), by deploying communication entities such as road side units (RSUs), smart gateways, and small cell base stations to construct a network topology. It deploys corresponding computing and storage units at wireless access points (WAPs) to provide personalized services to MUs. MUs can directly access computing services from edge servers located at WAPs, avoiding traffic bottlenecks in the core and backhaul network during routing, thus partially alleviating the cache and computational burdens on data centers. Additionally, MEC offers performance-optimizing technologies such as computation offloading (CO) [5] and edge caching (EC) [6]. Computation offloading, as a service optimization paradigm, allows MUs to delegate complex interactive computing tasks to edge nodes and receive computation results via the wireless downlinks between WAPs and MUs, thereby greatly alleviating the problem of high computational energy consumption in terminal devices with limited computational resources during service usage. Edge caching allows users to cache application data at communication nodes, enabling different MUs requesting the same service to directly upload locally generated real-time data for computation, avoiding the redundant transmission of the same application data and thus alleviating high latency and heavy load in the fronthaul network. As an evolution of traditional mobile base stations, the MEC architecture allows for the scheduling and management of parallel communication resources at the network edge layer. This provides a new solution for designing cache data-sharing strategies among MEC nodes using backhaul networks, further optimizing system performance. It also offers cloud service providers more flexible computation offloading solutions by changing the execution and storage locations of data to optimize system performance.