# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 1 Introduction
A dynamic preference model is constructed based on different time periods, and attribute decision making is used to select the best handover satellite. Ref. [11] addresses the frequent handover challenges in Low Earth Orbit (LEO) satellite networks by leveraging Long Short-Term Memory (LSTM) to predict satellite motion patterns and implement dynamic handover decision making. Ref. [12] proposes the second satellite selection algorithm that predicts the service time, link quality, and expected handover time based on the visible satellite information and selects the optimal selection combination considering the second satellite. This algorithm improve user's QoS and reliability when compared to other benchmark schemes. However, QoS does not fully reflect the user's QoE. For instance, while higher transmission rates (a QoS metric) may enhance throughput, they do not improve the QoE if no rebuffering occurs during playback. In addition, among the QoS indicators of satellites, handover failure seriously affects the quality of QoS. For buffered streaming media services, transient handover failures may not degrade QoE provided the buffer remains non-empty. The factors affecting QoE for streaming services are discussed in Section 3. For the QoE guarantee of streaming media services on the ground, considering the extremely complex and variable communication environment on the ground, there are two main types of schemes, which are optimized for bandwidth and buffer to optimize the QoE. Ref. [13] proposed to improve the QoE of video streaming through bandwidth allocation that is DASH-aware. Other work used bandwidth prediction [14] or predicted cellular network throughput for improved bitrate selection [15; 16] to maximize over a QoE metric. Meanwhile, other studies consider optimization for buffers. Refs. [17; 18] used buffer-based algorithms that select the optimal download bitrate based on buffer occupancy. In contrast, MPC [19], Pensieve [20], Oboe [21], etc., select bitrates as a function of buffer length and network throughput. However, in the LEO communication environment, the communication channel is more predictable than the terrestrial channel, especially in environments where satellite communication is needed, such as deserts, wilderness, oceans, etc. Therefore, the elevation angle change rule can be fully utilized to predict the channel to a certain extent so as to allocate reasonable communication resources under the circumstance of guaranteeing the QoE of users. In this paper, we consider the scheme design of access and handover of the user's streaming media service when it is transmitted in the system based on LEO satellite communication from the level of QoE. A simulation system is designed for a user's streaming media service provider to transmit streaming media packets using LEO satellites, and the flow from transmission to the playback of video or audio services is realized. The system consists of four parts representing the four states of the user, i.e., idle, playback, initial buffering, and rebuffering, in order to simulate various environments of the user during the use of the streaming service. We propose QoE-driven access and handover algorithms tailored to three user states (playback, initial buffering, and rebuffering) by leveraging streaming buffering mechanisms, thereby ensuring the QoE under resource-constrained satellite networks. Unlike QoS-based methods, our scheme adapts to streaming media states. Specifically, it predicts the channel capacity to estimate changes in buffer occupancy at user terminals, enabling optimal resource allocation to prevent buffer exhaustion while reserving bandwidth for critical handover events during network congestion. Finally, we evaluated the proposed scheme's performance using the simulation system and quantified QoE via Mean Opinion Score (MOS) comparisons against traditional handover methods.