## 1 Introduction
Similar to BS cooperation in terrestrial networks, multiple LEO satellites can also achieve cooperation, which compensates for the insufficient computing resources of a single LEO satellite's MEC server. The local satellite migrates some tasks to neighboring satellites, which can utilize the computing resources of neighboring satellites' MEC servers to compute tasks, thus reducing the task completion delay. Compared with local computing, task offloading introduces additional overhead, such as transmission delay. Moreover, the computing resources of MEC servers are relatively limited compared with the central cloud. Therefore, it is very important to make task offloading decisions and allocate computing resources properly. According to whether tasks are divisible, computation offloading can be divided into two categories, namely binary offloading and partial offloading [16]. Binary offloading involves integer programming, so the corresponding task offloading problem is generally NP-hard. Furthermore, assigning computing resources to tasks of different devices complicates the problem and increases the difficulty of algorithm design [17]. In this paper, we consider an STN consisting of IoT devices, a local satellite, and neighboring satellites. The tasks of the IoT devices within the coverage area of the local satellite are considered, and the neighboring satellites are regarded as computing nodes without considering the tasks of the IoT devices within their coverage areas. Neighboring satellites assist the local satellite in computing IoT devices' tasks by inter-satellite cooperation. We propose an inter-satellite cooperative task offloading and resource allocation scheme in an MEC-enabled STN, which minimizes the task completion delay for all IoT devices under the condition that the tasks are indivisible. The main contributions of this paper are as follows:  
* The system model of the MEC-enabled STN is established to provide MEC services in remote or disaster areas by utilizing inter-satellite cooperation. The joint task offloading and computing resource allocation problem is formulated to minimize the task completion delay and decomposed into a task offloading decision problem and a computing resource allocation problem. * An inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method is proposed. The optimal task offloading decision and computing resource allocation are obtained by the proposed scheme. * The performance of the proposed scheme is evaluated by comparing it with other baseline schemes with respect to the variation of some parameters. Simulation results demonstrate the performance gain of the proposed scheme. The rest of this paper is organized as follows. Section 2 introduces related works. Section 3 describes the system model. The task completion delay minimization problem for all IoT devices is formulated in Section 4. In order to solve this problem, the corresponding algorithm is designed in Section 5. In Section 6, simulations and an analysis are presented. Section 7 concludes this paper.