## 2 Pre-Scheduling Mechanism
### Pre-Scheduling Mechanism  
The BL is necessary for the video stream to be decoded. ELs are utilized to improve stream quality. Therefore, for high QoS for video streaming, we calculate the total number of BL that is required in a handover period for maintaining high QoS for video streaming. \[N_{BL}=(t_{r}+t_{ho}+t_{n})\times K_{s}\times m \tag{5}\]  
where \(t_{r}\) is the time interval from scheduling to starting handover (pre-scheduling time for handover). The starting time of scheduling is adjustable, and we will evaluate it in our simulation later. \(t_{ho}\) is the time during handover procedure. \(t_{n}\) is the delay time before new transmission (preparation time of scheduling with new eNodeB). \(K_{s}\) is the required number of video frames per second and m is the number of BL that is needed in each video frame. In Figure 2b, according to transmission data rate of the serving eNodeB, we construct a linear regression line \(d_{x}\left(t\right)\). Then, the amount of BL's data (transmitted from serving eNodeB and stored in the buffer of users) before handover has to be no less than \(N_{BL}\). \[\int_{t_{max}}^{t_{handover}}d_{x}\left(t\right)dt\geq N_{BL} \tag{6}\]  
Figure 2: Prediction for (**a**) time-to-trigger (TTT) of handover and (**b**) amount of data transmitted before handover. where \(t_{handover}\) is the TTT for handover. In the above inequality, the left part is the amount of data that the serving eNodeB can transmit before handover. According to the serving eNodeB capacity of transmission, we can dynamically adjust the transmission rate between BL and ELs. In Equation (6), while the inequality does not hold, it means the serving eNodeB cannot provide enough data for BL for maintaining high QoS for video streaming. Accordingly, the serving eNodeB merely transmits data for BL. On the contrary, while the inequality holds, the serving eNodeB can provide the data of BL and ELs simultaneously for desired quality of video service. In the following, we describe our mechanism of data rate adjustment between BL and ELs. The transmission rates of the BL and ELs are decreasing because the RSRP is degrading between the previous serving eNodeB and user. Hence, by the regression line \(d_{x}\left(t\right)\), we can define the total descent rate \(s\) (slope) of transmissions as  
\[s=\frac{\Delta\mathbf{y}}{\Delta\mathbf{x}} \tag{7}\]  
In Figure 3, because of the decreasing RSRP, the transmission rates of BL and EL are also decreasing with time unit respectively. Then, we let per time unit be \(t_{unit}\), that is,  
\[t_{0}=\ t_{1}=t_{2}=t_{3}=\ldots=t_{i}=t_{unit} \tag{8}\]  
Because of the limitative transmission rate of the serving eNodeB during a certain time interval, we have  
\[t_{unit}\cdot\left(d_{BL,i}+d_{EL,i}\right)\leq\int_{0+t_{unit}\cdot i}^{0+t_{unit }\cdot\left(i+1\right)}d_{x}\left(t\right)dt \tag{9}\]  
where \(d_{BL,i}\) and \(d_{EL,i}\) are the transmitted number of BL and ELs during time interval \(t_{i}\), respectively. In Equation (9), the total transmitted number for streaming video (left part) is necessarily less than or equal to the total number of data the serving eNodeB can provide (right part). Thus, the total descent rate of transmission per \(t_{unit}\) can be calculated as \(st_{unit}\). In this paper, for high QoS for video streaming, BL data has high priority for transmission. Furthermore, to achieve dynamically adjusting the transmission rate between BL and EL, we define the descent rate as  
\[K_{i}=\frac{d_{EL,\partial}}{d_{BL,0}} \tag{10}\]  
\(K_{i}\) is the proportion of the transmission rate between EL and BL during the time interval.