# A 5G-Based VR Application for Efficient Port Management
## 3 Application of 5G + VR in Smart Ports
### QoS Based System Optimization  
The core QoS requirements of vehicle communication service types are classified in Table 1. Through QoS tracking and prediction, the system can dynamically determine the optimal network path, select transmitted data, and allocate channel resources for different  
Figure 10: The architecture of intelligent container tally system.  
types of businesses. Based on the characteristics of the truck business and user QoE requirements, we propose optimal path planning model that is shown in Figure 11.  
According to the core QoS requirements, we perform QoE tracking and modeling for each communication link of the specific application. Using the set E to represent the set of links in the network, for any one transmission link e belonging to E, four attribute functions are defined, transmission link delay function delay(\(e\)), cost function cost(\(e\)), transmission link available bandwidth function bandwidth(\(e\)), and transmission link delay jitter(\(e\)). For any node n, four attributes are defined, the node delay function delay(\(n\)), the node cost function cost(\(n\)), the packet loss function packet loss(\(n\)), and the delay jitter  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & **Business Types** & \multicolumn{2}{c}{**Business Characteristics**} & \multicolumn{2}{c}{**Core QoS Requirements**} \\ \hline \(\bullet\) & Vehicle positioning and posture & \(\bullet\) & Low latency & \(\bullet\) & Upstream: 100 kbps \\ \(\bullet\) & Beacon & \(\bullet\) & Low upstream and downstream rates & \(\bullet\) & Downlink: 150 Mbps \\ \(\bullet\) & Traffic status & \(\bullet\) & Small data packets & \(\bullet\) & 20 ms end-to-end delay \\ \hline \(\bullet\) & \(\bullet\) & Urlink high speed & & & \\ \(\bullet\) & Video surveillance & \(\bullet\) & Large data packets & & \\ \(\bullet\) & Radar signals & \(\bullet\) & Continuous transmission & \(\bullet\) & 50 Mbps uplink \\ \(\bullet\) & Ship vision signals & \(\bullet\) & Long distance from the base station brings video & & \\  & quality degradation & & & \\ \hline \(\bullet\) & \(\bullet\) & Low data rates & & & \\ \(\bullet\) & Vehicle collaboration & \(\bullet\) & Small packets & \(\bullet\) & Random channel access \\ \(\bullet\) & Control system & \(\bullet\) & Intermittent & \(\bullet\) & Massive connections \\  & & \(\bullet\) & Large number of connected nodes & \(\bullet\) & \\  & & \(\bullet\) & Random channel accesses bring low channel utilization & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle communication service classification.  
Figure 11: Optimal path decision model.  
function delay_jitter(\(n\)) in the node. For some given source node s and destination node d are belonging to the set of nodes, _Pr(s,d)_ is the transmission path between the source node s and the destination node ground. The QoE constraints to be satisfied are shown below.  
\[f_{1}(P_{r}(s,d))=\sum_{c\in P_{r}(s,d)}delay(e)+\sum_{n\in P_{r}(s,d)}delay(n )\leq M_{1}\]  
\[f_{2}(P_{r}(s,d))=min\{\text{ bandwidth}(e),e\in P_{r}(s,d)\}\geq M_{2}\]  
\[f_{3}(P_{r}(s,d))=\sum_{c\in P_{r}(s,d)}\text{delay\_jitter}\ (e)+\sum_{n\in P_{r}(s,d)}\text{ delay\_jitter}\ (n)\leq M_{3}\]  
\[f_{4}(P_{r}(s,d))=1-\prod_{n\in P_{r}(s,d)}\left(1-\text{ packet\_loss}(n)\right)\leq M_{4}\]  
\[f_{5}(P_{r}(s,d))=cost(P_{r}(s,d))\]  
\(M_{1}\) represents the service constraint on the maximum end-to-end latency, \(M_{2}\) represents the minimum available bandwidth of the network transmission link, \(M_{3}\) indicates the maximum delay jitter that the system can tolerate, and \(M_{4}\) represents the maximum packet loss rate constraint. The QoE path selection algorithm looks for a path with the smallest network cost from all possible network paths (i.e., 4G, 5G, WiFi, optical fiber, network cable, etc.), represented as _Pr(s,d)_ if the four QoE constraint is met.