## 5 Numerical Results
### Performance Assessment  
Having observed the performance of the proposed grouping algorithm we now proceed to assess multi-layer VR service performance. We start with Figure 4 showing the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Description** & **Values** \\ \hline \(f_{c}\) & operational frequency & 73 GHz \\ \(B\) & number of available RBs & 264 \\ \(r_{B}\) & blocker radius & 0.4 m \\ \(h_{B}\) & blocker height & 1.7 m \\ \(h_{U}\) & UE height & 1.5 m \\ \(h_{A}\) & NR BS height & 4 m \\ \(v\) & UE speed & 1.5 m/s \\ \(P_{T}\) & transmit power & 2 W \\ \(N_{U}\) & UE antenna array configurations & 64 \\ \(N_{0}\) & density of noise & \(-84\) dBi \\ \(\lambda_{B}\) & density of blockers & 0.3 units/m\({}^{2}\) \\ \(\xi_{T}\) & path loss exponent & 2.1 \\ \(\lambda\) & intensity of video sessions from a UE & \(\frac{1}{3000}\) sessions/s \\ \(\mu\) & mean session length & 15 s \\ \(d_{M}\) & base layer downloading rate & 7.78 Mbps \\ \(\tilde{d}_{U}\) & enhancement layer downloading rates & \(\{19.78,25.81,31.96\}\) Mbps \\ \(Q\) & minimum UE to NR BS distance & 1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Default system parameters. Figure 3: ADR performance comparison. optimal BS deployment density as a function of UE density for 3 QoS profiles defined by \(\vec{p}_{U}\) and \(\lambda_{B}\) is set to 0.3. Recall that QoS profiles \(\vec{p}_{U}\) specifies the share of users that should receive \(l\)-th layer of quality enhancement. That is, the profile \((0.5,0.3,0.1)\) impose mild data rate requirements than the one with \((0.75,0.5,0.25)\). The latter, in its turn, is characterized by smaller data rate requirements than the one with \((0.9,0.8,0.7)\). By analyzing the data presented in Figure 4 one may observe that the scheme with \((0.5,0.3,0.1)\) is logically characterized with the least required NR BS density. On top of this, this QoS profile is also associated with the lowest increase in terms of the required NR BS density. Particularly, for the density of approximately 0.2 UE/m\({}^{2}\) and 1.0 UE/m\({}^{2}\) the associated NR BS densities are just 25 BS/km\({}^{2}\) and 90 BS/km\({}^{2}\), respectively. The latter values are as large as 150 and 250 for \((0.75,0.5,0.25)\) and \((0.9,0.8,0.7)\) QoS profiles, respectively. Also, logically, the stricter the QoS profile the higher the required BS density. The latter trend holds for all considered ranges of UE densities. A critical parameter affecting the performance of mmWave system is the blockers' density. To assess its effect on the performance of the proposed scheme for QoS-aware multi-layer VR distribution service we show the optimal NR BS deployment density as a function of \(\lambda_{B}\) in Figure 5, where the UE density is set to 0.3. Here, we observe clearly non-linear trends. More specifically, both \((0.5,0.3,0.1)\) and \((0.75,0.5,0.25)\) QoS profiles are only mildly affected by the blockers' density across the whole considered range. However, \((0.9,0.8,0.7)\) QoS profile leads to the larger increase in the amount of required NR BS reaching 110 BS/km\({}^{2}\) for \(\lambda_{B}=1.0\) bl./m\({}^{2}\). Note that the corresponding NR BS densities for \((0.5,0.3,0.1)\) and \((0.75,0.5,0.25)\) QoS profiles are just 60 BS/km\({}^{2}\) and 40 BS/km\({}^{2}\), respectively. Figure 4: Optimal NR BS density as function of UE density. Figure 5: Optimal NR BS density as function of blocker density. One of the critical advantages of the considered multi-layer VR service is that it allows implementing the basic layer by multicast service, thus, decreasing the demands to the rate at the air interface. To assess the efficiency of utilizing the multicast for delivering the base layer of the considered multi-layer VR service, we introduce the coefficient \(L_{R}\) specified as the ratio of unicast layers to the basic layer. In other words, demands of unicast layers are multiplied by coefficient \(L_{R}\) resulting in varying unicast traffic load.