## 2 Related Works
### Task Offloading and Resource Scheduling Algorithms for LEO Networks  
In addressing the challenges of task offloading and resource scheduling within LEO network environments, Ref. [34] examines the varying aspects of task segmentation and the energy priorities of edge devices. The study introduces an energy-efficient task offloading and resource scheduling algorithm to minimize both overall delay and energy usage.  
Studies [35; 36] put forward joint task offloading and resource scheduling algorithms leveraging deep reinforcement learning. Specifically, Ref. [35] employs a distributed algorithm grounded in convex optimization to tackle the task offloading and resource scheduling issue, recasting it as a direct optimization problem of computing resource scheduling for LEO satellites and simulating the system to maintain high energy efficiency. Meanwhile, Ref. [36] utilizes a deep reinforcement learning approach for resource allocation, addressing task offloading to the edge device and task segmentation issues, and applies a deep reinforcement learning strategy to identify the optimal offloading approach to minimize delay and energy consumption at the edge device.  
Moreover, Refs. [37; 38] suggest joint task offloading and resource scheduling frameworks using deep reinforcement learning. For instance, Ref. [37] proposes an integrated strategy for computing resource allocation and task offloading that adapts to dynamic changes in the network environment to enhance energy efficiency long-term, employing deep reinforcement learning to modulate the offloading strategy dynamically. Meanwhile, Ref. [38] evaluates LEO network architecture with edge computing, focusing on minimizing user task processing delay and energy use at the edge node, and proposes a comprehensive strategy for task offloading and computing resource scheduling to achieve long-term reductions in delay and energy consumption. Furthermore, Ref. [39] considers dynamic shifts in the locations of edge devices, satellites, and the network setting, using a deep reinforcement learning algorithm to optimize user association, offloading, computing, and communication resource allocation strategy for minimal long-term delay and energy use. Additionally, Ref. [40] presents a joint task offloading and resource scheduling strategy tailored for LEO edge computing aimed at minimizing system costs, with simulation outcomes indicating that the proposed strategy can notably decrease the average system cost.  
Despite significant advancements in LEO satellite task offloading research, there remains a substantial gap in fully leveraging satellite collaboration for efficient resource utilization. Existing studies, summarized in the Table 1, primarily focus on optimizing task execution delay and energy efficiency without accounting for the dynamic nature of LEO networks and the potential of satellite cooperation. To address these gaps, our research makes the following unique contributions. We propose a groundbreaking integration of Mobile Edge Computing (MEC) technology with LEO satellite communication networks. This integration considers unique constraints such as onboard computing capabilities, satellite-ground and inter-satellite link capacities, and traffic conservation laws, which have not been collectively addressed in prior works.