# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 3 Design and Implementation of Video Transmission
### Terminal-Edge Collaborative Computing  
The 5G module is connected to the front-end camera of the unmanned vehicle, and it is powered by the vehicle itself. The camera captures video information using its shooting function and the 5G module. This information is then transmitted through the 5G wireless network to both the 5G base station and the server of the remote-control subsystem for storage, analysis, and processing. Remote operators can access the remote-control subsystem to perform real-time monitoring, control management, and other relevant functions related to the front-end camera. In this setup, the deployment of mobile edge computing (MEC) units at the edge of base station becomes crucial for enhancing the Quality of Experience (QoE), especially due to the limited computing power of 5G terminals. By offloading computing tasks to edge units, faster and more efficient processing can be achieved [34]. This optimization resource utilization not only reduces latency but also improves responsiveness, thus enhancing the overall user experience. The flow of data transfer is depicted in Figure 5. The vehicle terminal offloads some computing tasks to MEC, reducing the total computation time through collaborative work.  
Figure 4: UE architecture for transmitter switching.  
The processing of each task involves two stages. First, the task is handled locally on the vehicle terminal, and then the second part is offloaded to an edge computing unit. Once the calculations are completed, the edge computing unit sends the results back to the vehicle. The system operates under two scenarios, which are depicted in Figure 6, where BS 1, BS 2, MEC 1, and MEC 2 represent the neighboring base stations and their corresponding edge units. T\({}_{1}\) and T\({}_{2}\) denote the respective instants when this task starts and completes.  
* If the vehicle remains within the coverage range of the original base station, the results can be directly returned to the vehicle.
* If the vehicle enters the coverage range of another base station, coordination between base stations is necessary to ensure the results are sent back to the vehicle.  
Figure 5: Data flow of onboard video transmission.  
Figure 6: Two scenarios of computing returning: (**a**) within the same base station; (**b**) across base stations.  
Assuming the data size for a single task is \(B\), and the local processing ratio is \(\mu\), the amount of data offload is \((1-\mu)B\). Since the local computation and offloading processes are parallel, the time required to complete this task can be expressed as follows:  
\[T_{task}=max\Big{(}T_{local},T_{offload}+T_{mcc}\Big{)} \tag{4}\]  
where \(T_{task}\), \(T_{local}\), \(T_{offload}\), and \(T_{mcc}\) indicate the total computing time, local computing time, offloading time, and edge computing time, respectively. The respective formulas for \(T_{local}\), \(T_{offload}\), and \(T_{mcc}\) are as follows:  
\[\begin{split} T_{local}&=\alpha\mu B/f_{local}\\ T_{offload}&=\beta(1-\mu)B/R\\ T_{mcc}&=\alpha(1-\mu)B/f_{mcc}\end{split} \tag{5}\]  
where \(\alpha\) is a constant determined by computational complexity, and \(\beta\) is a constant for the overhead of uplink transmission, \(f_{local}\) and \(f_{mcc}\) are the computational powers of the local and edge unit, respectively, and \(R\) is the uplink speed. If there are \(k\) tasks in total, then the average latency can be expressed as follows:  
\[T_{avg}=\sum_{i=1}^{k}T_{task,i} \tag{6}\]  
It can be observed that the average latency \(T_{avg}\) is influenced by factors such as the offloading rate, computation allocation, and uplink speed. It can be optimized by optimizing the offloading rate to achieve the best performance in specific scenarios.