## 2 Related Works
### Computation Offloading Strategy  
The real-time computation offloading (RCO) can enhance the QoE of interactive gaming [11]. In this scenario, MEC servers would process real-time action data offloaded by players (MUs) and render them into corresponding in-game scenes. The rendered data are then compressed via a video encoder and transmitted back to users through video-streaming. The RCO strategy allows servers to wisely offload service requests from multiple users, maximizing overall user satisfaction. RCO can also be applied in energy-constrained drones [12] and wearable devices [13] to reduce the computational overhead of local devices. Complex neural networks or machine learning computations can drain battery life for these devices. Additionally, requests from similar types of devices often exhibit popularity (e.g., health monitoring, object detection, and path planning algorithms). The RCO algorithm can select a strategy that maximizes offloading benefits based on the application categories stored on the server and the user cluster's requests, thereby reducing the computational energy consumption of MUs and extending device battery life.  
Multiple mobile users can achieve resource-sharing and collaborative computing, while nearby MUs may request similar tasks [14]. Based on this scenario, a fine-grained collaborative computing offloading and caching strategy is proposed to minimize the overall execution latency of MUs within the network [15]. Additionally, the concept of a call graph is utilized to model the offloading and caching relationships among MUs. It is noteworthy that [16] considers software-fetching and multicasting in network modeling, mathematically characterizing the processes of data uploading, task execution, and computation result downloading to minimize cache and weighted deadline as optimization objectives. They employ a joint algorithm combining ADMM and a penalty convex-concave procedure to obtain the optimal offloading strategy.  
The ADMM algorithm is also applied in [17] to obtain distributed offloading decisions. In this work, the authors propose a computation offloading scheme where computational tasks generated by ground users can be computed locally, on Low Earth Orbit (LEO) satellites, or on cloud servers. The authors also consider the limited computational capabilities and coverage time of each LEO satellite. They subsequently investigate the optimization problem of minimizing the total energy consumption of ground users, which is discrete and non-convex, and convert it into a linear programming problem.  
In Time-Division Multiple Access (TDMA)-based MEC systems, a partial offloading strategy based on an iterative heuristic algorithm is proposed [18] to minimize the total energy consumption of MUs while ensuring the task delay constraints. This strategy jointly optimizes task offloading rates, channel allocation, and MEC computing resource allocation. The authors decompose the problem into a series of offloading subproblems and design a two-stage algorithm to iteratively solve the offloading task set until achieving the minimum energy consumption.  
When addressing performance optimization problems with computation offloading algorithms, it is observed that the discussion on the economies of scale brought by multi-server clusters is insufficient. Most articles only conduct research based on single-server scenarios, while in reality, MEC servers are often densely deployed in scenarios such as streets, malls, and schools to provide services to users. This cluster effect can be applied to the design of distributed network models and data-sharing algorithms. Wired backhaul links between edges can transmit large amounts of real-time data, which are also beneficial for the on-demand allocation of cached content related to real-time tasks.