This paper focuses on downlink packet scheduling for streaming video in Long Term Evolution (LTE). As a hard handover is adopted in LTE and has the period of breaking connection, it may cause a low userperceived video quality. Therefore, we propose a handover prediction mechanism and a prescheduling mechanism to dynamically adjust the data rates of transmissions for providing a high quality of service (QoS) for streaming video before new connection establishment. Advantages of our method in comparison to the exponential/proportional fair (EXP/PF) scheme are shown through simulation experiments.
1. Introduction
2. PreScheduling Mechanism
2.1. Handover Prediction
where Pˆi is the predictive value of RSRP at time ti, and a and b are coefficients of the linear regression equation. Then, we use the least squares (LS) method to deduce a and b. The method of LS is a standard solution to estimate the coefficient in linear regression analysis.
where Pi is the measured value of RSRP at time ti. The least squares method is to try to find the minimum of S, and then the minimum of S is determined by calculating the partial derivatives.
where T¯¯¯= ∑ni=1tin and P¯¯¯= ∑ni=1Pin. If there are several neighbor eNodes, we select the eNodeB with the maximum variation of RSRP (maximum slope) as target eNodeB. In Figure 2a, we can see that while RSRPSeNB=RSRPTeNB, the handover procedure is triggered. We have trigger time tt=a1−a2b2−b1.
2.2. PreScheduling Mechanism
where tr is the time interval from scheduling to starting handover (prescheduling time for handover). The starting time of scheduling is adjustable, and we will evaluate it in our simulation later. tho is the time during handover procedure. tn is the delay time before new transmission (preparation time of scheduling with new eNodeB). Ks 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 dx(t). 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 NBL.
where thandover 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 dx(t), we can define the total descent rate s(slope) of transmissions as
where dBL,i and dEL,i are the transmitted number of BL and ELs during time interval ti, 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 tunit can be calculated as s⋅tunit. 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
3. Performance Evaluation
3.1. The Effect of the Prediction Mechanism
3.2. Base Layer Adjustment
4. Conclusions
Author Contributions
Conflicts of Interest
Abbreviations
LTE 
Long Term Evolution

EXP/PF 
exponential/proportional fair

3GPP 
3rd Generation Partnership Project

RR 
round robin

MR 
maximum rate

PF 
proportional fair

LA 
link adaptation

MLWDF 
MaximumLargest Weighted Delay First

HOL 
headofline

SVC 
scalable video coding

BL 
base layer

ELs 
enhancement layers

RSRP 
Reference Signal Receiving Power

ES 
exponential smoothing

TTT 
timetotrigger

LS 
least squares

QoE 
qualityof experience

SPS 
semipersistent scheduling

PRBs 
physical resource blocks

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