# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 3 Simulation System Design for Handover and Access of Streaming
The model evaluates the performance and quality of real-time networks (including codecs) for audio, video, and audiovisual, including the impact of coding bitrate and transmission issues that lead to long initial buffering and stalling events. Considering that metrics such as bitrate are also related to the choice of multimedia application providers and the users themselves, this paper only considers the model's impact on the MOS score of initial buffering and stalling events in the middle of playback to react to the user's QoE. Quality impacts \(DegT0\) from initial buffering  
\[DegT0=\left\{ \begin{array}{cc}\max(\min(d_{1}\times\lg(T_{0}+d_{2}),4),0)&T_ {0}>1-d_{2}\\ 0&else\end{array} \right., \tag{3}\]  
Figure 3: Streaming service simulation algorithm structure. where \(T_{0}\) is the initial loading time of the video in seconds. \(d_{1}\) and \(d_{2}\) are fixed coefficients whose values are referenced in Table 2. The impact of stalled buffering events in playback on quality \(DegStall\) is  
\[DegStall=max(min(s_{4}+s_{1}\times exp((s_{2}\times L+s_{3})\times N),4),0), \tag{4}\]  
where \(L\) is the average rebuffering time in seconds (excluding the initial loading time), and \(N\) is the number of stalls occurring (excluding the initial buffer). The values of the coefficients of Equations (3) and (4) are given in Table 2. These coefficients are fitted and validated based on the ITU database-set; refer to [24] for details. The final evaluation score \(PBufInd\) (presented on a scale of 1 to 5) for video buffering wait is calculated as follows:  
\[PBufInd=5-max(min((DegStall+DegT0),4),0) \tag{5}\]  
As can be seen from the evaluation system above, the reception rate of the user terminal is directly associated with the streaming QoE. The magnitude of the reception rate will directly affect the initial loading time \(T_{0}\) of the video or audio and the average rebuffering time L during playback, thus reducing the user's video playback experience. At the same time, from the exponent in Equation (4), it can be inferred that if the quality of the user streaming service is to be guaranteed, the average user download rate should be as high as possible above the bit rate of streaming media to avoid data exhaustion in the buffer; otherwise, the user will suffer playback rebuffering, which will lead to a significant decrease in QoE [27]. The strategy of many streaming media content platforms is also to try their best to avoid mid-play stagnation [28], and these platforms are designed to have redundancy of up to 30%-40% of the transmission rate; in other words, when the platform detects that the current reception rate is not higher than 130%-140% of the current video bitrate, it will immediately reduce the resolution of the video itself to lower the video's bitrate. In this paper, we will design the access and words; when the platform detects that the current reception rate is not higher than 130%-140% of the current video bitrate, it will immediately reduce the resolution of the video itself to lower the video's bitrate. In this paper, we will design the access and handover algorithms according to the characteristics of these streaming media services, and we will also provide some redundancy in the transmission rate, considering that satellite environments are more stable than terrestrial, so the access and handover scheme in this paper is set at 10% redundancy in channel capacity.