## 1 Introduction
First, although there have been numerous innovations in the wireless communication and networking fields over the past few years, allowing mobile users to access cloud data at extremely fast transmission rates, the current network architecture still suffers from significant propagation delays, which are unacceptable for latency-sensitive applications. For example, in video applications, retrieving video resources from the cloud can lead to long initial buffering times and video playback stuttering. Second, while the emergence of heterogeneous satellite networks has expanded the network's reach, it has also increased its complexity. Therefore, when transmitting video streams in heterogeneous satellite networks, it is necessary to measure and predict user traffic and proactively allocate and plan network resources based on the network status to ensure users' video quality demands and reduce the pressure on network operations and maintenance. Finally, the simultaneous presence of multiple types of networks will lead to spectrum resource scarcity, severely limiting the future development of heterogeneous satellite networks. Given the non-renewable nature of spectrum resources, spectrum sharing is one of the inevitable solutions [7]. However, spectrum sharing also introduces new issues, such as co-channel interference between heterogeneous networks, especially the interference of low Earth orbit satellite networks with high Earth orbit satellite networks, because high orbit satellites have absolute priority to ensure their service quality is not affected by low orbit satellites [8; 9]. Therefore, in heterogeneous satellite networks based on video stream transmission, resource allocation and interference coordination still require further research. Compared to traditional terrestrial cellular networks, heterogeneous satellite networks that integrate MEC, in-network caching, and SDN can provide more efficient services to users. In this network, users can first request services from edge nodes equipped with caching and computing capabilities. If the edge nodes cannot meet the user's needs, the user can still access cloud server resources through the satellite backhaul network. The entire network's traffic planning and resource scheduling are uniformly controlled by SDN technology. However, due to the complexity of heterogeneous networks and the mix of resources, how to allocate resources to meet each user's requests still requires further research. Additionally, the issue of co-channel interference in heterogeneous satellite networks also needs to be addressed. Thus, the paper proposes a heterogeneous satellite network integrating MEC, in-network caching, and SDN to enhance service efficiency over traditional networks. The main contributions are summarized as follows. * This paper proposes to tackle resource allocation and interference coordination challenges in dual-layer satellite networks combining LEO and GEO satellites. * A mathematical optimization problem is formulated to optimize resource allocation while minimizing co-channel interference. An ADMM-based distributed algorithm is proposed to solve the optimization problem efficiently. * The algorithm decomposes the problem into subproblems for users, service nodes, and the network, allowing distributed optimization. The incorporation of MEC (Mobile Edge Computing), in-network caching, and SDN (Software-Defined Networking) enhances service efficiency in the satellite network. * Simulation results show that the proposed algorithm improves network performance by efficiently managing resources and reducing interference. The paper is structured as follows. Section 2 reviews existing literature on heterogeneous networks, satellite communications, and resource allocation strategies, emphasizing the need for integrated network architectures. Section 3 presents the formulation of the resource allocation and interference coordination problem within heterogeneous dual-layer satellite networks.