# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 4 Proposed PrOEXPRule Scheduler Design
### Proposed Optimisation Exponential Rule (PrOEXPRule) Scheduling  
The algorithm proposed in this study is intended to enhance video stream data using properties such as PLR, fairness, throughput, and delay by addressing jitter/delay \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of Simulators & 5 \\ Number of Clusters & 1 \\ Number of Cells in a Cluster & 4 \\ Initial User Count & 1 \\ User Interval & 5 \\ Maximum Users & 30 \\ Bandwidth & 5 MHz \\ RB Numbers & 25 \\ Slot Duration & 0.5 s \\ Scheduling Time (TTI duration) & 1 ms \\ Cell Radius & 1.5 km \\ Modulation Scheme & QPSK, 16 QAM, 64 QAM \\ Mobile Speed & 120 s \\ User Speed & 3 km/h \\ Video Bitrate & 256 kbps \\ Max Delay & 0.1 s \\ Flow Duration & 120 s \\ Simulation Duration & 120 s \\ Frame Structure & FDD \\ Video Bitrate & 242 kbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. characteristics and considering the maximum permissible delay for every packet. The eNB base-station schedule provisions network resources depending on the user count present at the scheduling block (SB), which represents a single 1 ms TTI. An SB comprises two resource blocks (RB), which, in turn, comprise a subcarrier sequence with several OFDM symbols. For each TTI, the eNodeB resource scheduler performs allocations of radio resources according to the demands of active users, all of whom are competing for scarce network resources according to certain criteria. If the eNodeB has sufficient resources to meet all user demand, no problems will arise in the allocation of resources. Conversely, if the eNodeB has less resources than that required to meet all user demand, the scheduler will have a primary role in allocating resources. In this study, the key role of the eNodeB downlink scheduling algorithm is to lessen packet loss in the user network queue by analysing the recorded jitter values for all transmitted packets, in addition to applying the delay priority technique, in which fundamental QCI properties are used for specifying the maximum permissible packet delay and packet loss. User prioritisation is performed on the basis of the nearer-to-deadline approach. It is feasible to maintain a packet loss ratio below the threshold by polling the HOL delay corresponding to every user. Packets proximal to the delay threshold are given higher priority. Generally, and for efficient decision-making in resource allocation in the downlink, the scheduler typically compares the metric values of each and every UE to every corresponding RB, RB \(k_{th}\), which is allocated to the UE contingent on comparisons between metrics for the \(t_{th}\) user with the largest \(M_{i,k}\) value, with use of the following expression [24]. \[M_{i,k}=max_{i}\ M_{i,k}, \tag{8}\]  
In addition to defined simulation parameters in Table 1, the proposed scheduling takes into account the channel state by considering the jitter value, and its metric is computed by considering the mean transmission rate for every flow in real time. In the case of one buffer, there are F packet flows that are stored in a queue with different parameters, which may be the packet length distributions and the arrival time. With the aim of computing the jitter for flow f and as per [7], all flows are considered to have arrival rate \(\lambda_{i}\) with distributions in service time \(\mu_{i}\). The total arrival rate is:  
\[\lambda=\sum_{i=1}^{m}\lambda_{i}, \tag{9}\]  
For the flows F, jitter in a single cell can be considered as convergent to:  
\[J_{i}\approx\frac{1}{\eta}\left[1-e^{\frac{-\eta}{\lambda_{i}}}\left(\frac{ \eta}{\lambda_{i}}+e^{\frac{-\eta}{\lambda_{i}}}\right)\right], \tag{10}\]  
where \(=\lambda-\mu\).