## 5 Simulation Results and Analysis
This is because higher computing power enables the network nodes to handle more tasks, allowing more users to retrieve video resources nearby, thus improving network performance. Moreover, the observed QoE improvement is attributed to the efficient allocation of resources and effective interference coordination. By optimizing power control and frequency allocation, our algorithm minimizes co-channel interference between GEO and LEO satellites. This leads to enhanced spectral efficiency and higher data rates for users. Specifically, the dynamic adjustment of resource allocation in response to network conditions ensures that users experience consistent and improved service quality, even as the network scales. When the computing capacity reaches a certain threshold, the proposed scheme begins to stabilize. This is due to other limiting factors in the network, such as cache hit rates and link resources, preventing further performance improvement. The stabilization of performance at higher computing capacities (as seen in Figure 4) indicates diminishing returns beyond a certain threshold. This insight can guide network planners in optimizing \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline GEO orbit height & 35,786 km \\ LEO orbit height & 1200 km \\ Earth radius & 6371 km \\ Orbit inclination angle & 87\({}^{\circ}\) \\ LEO satellite antenna gain & 25 dBi \\ GEO satellite antenna gain & 25 dBi \\ LEO ground station antenna gain & 45 dBi \\ GEO ground station antenna gain & 30 dBi \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation setups. resource allocation by balancing computing capacity with other factors, such as caching, and link bandwidth, to achieve cost-effective deployments. Figure 5 shows the relationship between the number of access points (APs) and the average vMOS of users. It can be observed that as the number of access points increases, the average vMOS of users improves. This is because an increase in the number of access points enhances the network's service capacity by providing more resources and offering better access environments for users. Additionally, more access points improve network coverage and channel conditions, further enhancing user experience. Increasing the number of base stations reduces the number of users served by each base station. This alleviates traffic congestion, allowing each user to experience better service quality due to more available resources per user. More base stations enable the network to implement frequency reuse more effectively. By assigning the same frequency bands to non-adjacent cells, the network maximizes spectral efficiency while minimizing co-channel interference. This efficient use of spectrum resources contributes to higher data throughput and improved QoE. Next, Figures 6-8 illustrate the average MEC server load under different network settings. To examine the impact of network architecture, we compare a solution where MEC servers are only deployed at terrestrial base stations and another where MEC servers are only deployed at satellites. Figure 6 shows the relationship between the number of users and the average load on MEC servers. In the satellite service solution, the average load on MEC servers is consistently high due to the extensive coverage area of satellites. As the number of users increases, the MEC server load also increases due to the additional tasks the network needs to handle. Thus, as the number of users increases, the MEC server load continues to rise. Figure 4: Average user vMOS for different computing capacities. Figure 5: Average user vMOS for different numbers of access points. Figure 6: Average MEC server load for different numbers of users. Moreover, Figures 5 and 6 demonstrate the effectiveness of the proposed algorithm in minimizing co-channel interference between GEO and LEO satellites. This is particularly critical in heterogeneous satellite networks where spectrum sharing is inevitable.