## 5 Simulation Results and Analysis
The results suggest that the proposed strategy can enable more efficient spectrum utilization  
Figure 8: Average MEC server load for different computing capacities. Figure 7: Average MEC server load for different cache capacities. while maintaining service quality. In practice, this means that satellite operators can adopt the proposed algorithm to support higher user densities and data rates without compromising network stability. Figure 7 demonstrates the relationship between the cache capacity of service nodes and the average load on MEC servers. As cache capacity increases, the MEC server load also increases. This is because service nodes with higher cache capacities store more video files, improving the hit rate and increasing the likelihood that users retrieve video resources from service nodes, therefore increasing the computation load on the MEC servers. Figure 8 shows the relationship between the computational capacity of MEC servers and their average load. As the computing power increases, the load on MEC servers gradually decreases. This is because although more tasks can be handled with greater computational power, the number of users and their demand remains constant in this simulation. Hence, once the computational capacity surpasses the demand, the average load on MEC servers decreases. The proposed interference coordination strategy is integral to the ADMM-based algorithm, as it mitigates co-channel interference among multiple satellites and user terminals. By coordinating resource usage, the algorithm ensures that interference levels remain within acceptable limits, therefore enhancing overall network performance. Simulation results indicate that without effective interference coordination, the network experiences increased latency and reduced throughput, underscoring the necessity of this strategy in achieving optimal resource allocation. The improvements in user QoE, as shown in Figures 7 and 8, underscore the practical benefits of dynamic resource allocation and interference coordination. For instance, the ability to dynamically adjust resource allocation based on network conditions ensures consistent service quality, even under varying traffic loads. This is particularly relevant for applications such as live video streaming and remote sensing, where maintaining high QoE is critical for user satisfaction and operational success. The inherent advantages of LEO satellites, including their ability to perform data transmission with lower latency, make them particularly advantageous for applications such as live video streaming and real-time data processing. The scalability of the proposed algorithm, as demonstrated by its performance across different network sizes and configurations, demonstrates its potential for real-world deployment. By reducing computational complexity while ensuring effective resource allocation, the ADMM-based strategy can be implemented in large-scale satellite networks without incurring excessive computational overhead. This makes it a viable solution for next-generation satellite communication systems. Although we have not explicitly graphed interference-related metrics (e.g., SINR, interference probability) in the outcome figures, the ADMM-based scheme inherently addresses co-channel interference by coordinating resource usage among multiple satellites and user terminals. The improved QoS metrics (notably throughput and latency) corroborate that our allocation decisions prevent severe interference conditions and maintain service quality. Specifically, by incorporating interference factors into our objective and constraints, the proposed algorithm actively confines interference levels to permissible ranges. Future research efforts could place additional emphasis on quantifying these effects through dedicated metrics, building on the foundational resource coordination framework presented here.