# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Minimum Distortion Optimization Performance
However, with less bandwidth usage, the minimum distortion optimization algorithm can obtain a somewhat comparable PSNR before applying the PAR. When the estimated PAR is applied, it alters the total transmission budget \(SNR_{chk}\) in (17) according to the optimal ratio, affecting \(\rho_{i}^{*}\) and \(K^{*}\) for that reference viewpoint texture and depth map and eventually leading to a PSNR enhancement. For instance, in Figure 3d when \(SNR_{chk}=0\) dB, for the overall quality of the 3D video in SoftCast, the bandwidth usage for each reference viewpoint texture and depth map was 2048, and the achieved PSNR was 32.379 dB. However, the minimum distortion algorithm achieved a PSNR of 33.428 dB with only 88/2048 = 4.3% and 56/2048 = 2.7% of the texture and depth bandwidth usage. After applying the optimal PAR, the performance improved to 34.358 dB with a bandwidth usage of 6% and 1.4%. Furthermore, due to the flat tail of the minimum distortion curve, sacrificing a slight margin of the PSNR (e.g., \(\tau=98\%\) of the highest PSNR), reduced the bandwidth usage further from 124 to 39 chunks for texture and from 30 to 8 chunks for the depth map. These figures show that the proposed resource control scheme saves more bandwidth usage at low \(SNR_{chk}\). Hence, it is more suitable when the transmission power budget is limited or under inferior channel conditions. Table 3 lists the bandwidth usage for different 3D video standards. The distortion-resource and resource-distortion algorithms [31] were modified to ensure a fair comparison. Therefore, instead of transmitting three reference viewpoints, only two were transmitted, and the number of frames in each GOP was increased from four to eight. Since our proposed resource control scheme and the algorithms in [31] exploit different correlation methods, i.e., 3D-DCT and 5D-DCT, the adopted chunk size differs for each scheme, leading to a different number of chunks. Hence, the percentage of the total bandwidth used in each case is calculated, to compare the bandwidth resource usage. The proposed method remarkably decreases the bandwidth usage while achieving satisfactory results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**\(SNR_{clk}\) = 20 dB**} \\ \hline
**3D Video** & **SoftCast** & **Distortion** & **Proposed Resource** \\
**Sequences** & **SoftCast** & **Resource Algorithm** & **Control Scheme** \\ \hline Balloons & 100\% & 67.5292\% & 51.1108\% \\ Kendo & 100\% & 60.0586\% & 45.8862\% \\ Newspaper & 100\% & 65.1855\% & 48.3398\% \\ Dancer & 100\% & 89.3554\% & 56.4208\% \\ gtFly & 100\% & 91.5039\% & 49.1577\% \\ PoznanHall2 & 100\% & 56.9824\% & 34.9609\% \\ PoznanStreet & 100\% & 57.0801\% & 49.9633\% \\ Shark & 100\% & 95.0195\% & 83.1665\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(b)**\(SNR_{clk}\) = 15 dB} \\ \hline
**3D Video** & **SoftCast** & **Distortion** & **Proposed Resource** \\
**Sequences** & **SoftCast** & **Resource Algorithm** & **Control Scheme** \\ \hline Balloons & 100\% & 36.9629\% & 19.5922\% \\ Kendo & 100\% & 30.6641\% & 18.1274\% \\ Newspaper & 100\% & 33.8379\% & 17.6775\% \\ Dancer & 100\% & 73.3398\% & 40.8203\% \\ gtFly & 100\% & 75.0977\% & 41.6748\% \\ PoznanHall2 & 100\% & 37.5000\% & 21.6919\% \\ PoznanStreet & 100\% & 34.6191\% & 24.9146\% \\ Shark & 100\% & 84.9609\% & 59.1309\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bandwidth usage comparison for different 3D video sequences. Figure 3: Bandwidth usage savings and PAR influence: (**a**) \(SNR_{clk}\) = 15 dB; (**b**) \(SNR_{clk}\) = 10 dB; (**c**) \(SNR_{clk}\) = 5 dB; (**d**) \(SNR_{clk}\) = 0 dB.