# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 5 Experimental Results and Discussion
### Result Analysis
First of all, the cost of all schemes decreases as the communication bandwidth increases, except for that of the LCO scheme. This is because the LCO scheme transmits only at the UEs, and a change in the network communication bandwidth does not affect it. Additionally, the average cost of LCO schemes almost does not change. However, the cost of other schemes decreases as the communication bandwidth increases because every UE is able to allocate more bandwidth and the delay and energy consumption of communication transmission is also reduced. The cost of the proposed algorithm is reduced by about 49.51% compared with the JVFRS-TO-RA-DQN-OMA scheme at \(W=6\) MHz, and by about 34.17% compared with the TO-RA-DQN-NOMA scheme at \(W=6\) MHz. Figure 4 depicts the effect of the different computational capacities of the MEC server on the cost function. We take 1500 experiments and average the experimental data in a scenario with \(k=4\), \(M=10\), \(W=10\) GHz, \(\eta_{m}^{min}=200\times 200\) px and \(F\) ranging from 2 MHz to 12 MHz. Due to the fact that the UEs do not utilize the computing resources of the MEC server, the LCO schemes will not change as the computational capacity of the MEC server increases. However, the other schemes are reduced as the computational capacity of the MEC server increases since more computer source is allocated for MEC, with the computation time also being shortened accordingly. It is obvious that the average cost is mostly affected by other elements when the computational capacity of the MEC server is much bigger than the computational capacity of the UEs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **LCO** & **ECO-OMA** & \begin{tabular}{c} **JVFRS-TO-RA-** \\ **DQN-OMA** \\ \end{tabular} & \
\begin{tabular}{c} **TO-RA-DQN-** \\ **NOMA** \\ \end{tabular} &
\begin{tabular}{c} **MA-NOMA** \\ \end{tabular} & **Proposed** \\ \hline The bandwidth of & 1.2 & 1.2 & 1.2 & 3 & 3 & 3 \\ subchannel (MHz) & 644.54 & 801.49 & 323.42 & 251.77 & 409.66 & 167.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average latency of six schemes. Figure 3: The effect of communication bandwidth on the cost. Except for the delay and energy consumption, the video analytic accuracy is also affected by the video frame resolution. We modify the minimum video frame resolutions in this experiment to estimate the influence of resolution on accuracy. We take 1500 experiments and average the experimental data in a scenario with \(k=4\), \(M=10\), \(F=10\) GHz, \(W=10\) GHz, and \(\eta_{m}^{min}\) ranging from 200 \(\times\) 200 px to 700 \(\times\) 700 px. When calculating the accuracy, we assume that the system can detect all objects when the video frame resolution is 700 \(\times\) 700 px. The video frame resolution is lower than 700 \(\times\) 700 px, and the optimized resolution is between the minimum and the maximum video frame resolutions. The video frame resolution of the LCO scheme, the ECO-OMA scheme, and the TO-RA-DQN-NOMA scheme is identified as the average of the minimum and the maximum video frame resolutions. Figure 5 shows the effects of different minimum frame resolutions on the average delay. As illustrated in Figure 5, the average delay of all the algorithms increases as the minimum video frame resolutions change, except for the MA-NOMA. This is because the MA-NOMA always keeps the highest resolution, and the change of minimum frame resolution does not affect it. The proposed algorithm maintains minimum average delay, which means the proposed algorithm can adjust the resolution of video frame adaptively to reduce the system average delay. The average delay of the JVFRS-TO-RA-DQN-OMA scheme is higher than that of the proposed algorithm, which means that the delay of the NOMA enabled MEC system is superior to that of the OMA. Figure 4: The effect of computational capacity of MEC on the cost. Figure 5: The effect of minimum frame resolution on average delay. Figure 6 shows the effects of different minimum frame resolutions on video analytic accuracy.