# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Target Distortion Optimization Performance
#### 5.3.2 Bandwidth Distortion Optimization Performance  
Similarly, assume the sender delivers about 30 GOPs to users whose demanded quality is 35 dB, under a transmission power constraint \(SNR_{chk}=10\) dB. Figure 5 shows the bandwidth allocation curve for reference viewpoints for different video sequences. Compared with the large chunk number \(n=8192\) in one GOP, the total bandwidth usage fluctuates at a low and steady level. The bandwidth usage for the depth map can be less than three chunks in some GOPs. Therefore, it will be practical to allocate slightly more bandwidth resources (i.e., 4%), to avoid running Algorithm 3 several times, which will reduce the computational cost.  
Figure 6 presents the bandwidth usage saving obtained while achieving the target PSNRs. The conventional SoftCast exhausts all the available resources to achieve a good PSNR, unaware of the user's predefined distortion. However, the target distortion algorithm approaches the user's predefined PSNR with a lower bandwidth usage. The performance is slightly enhanced when the optimal PAR is applied. Based on Algorithms 2 and 3, the optimal PAR does not influence the power and bandwidth allocation. Nonetheless, provided the optimal PAR adjusts the power assigned jointly to texture and depth map frames in the pseudo-analog transmission, as shown in Figure 1, it still can influence the overall quality of the 3D video at the receiving end.  
For example, in Figure 6c, when the power budget \(SNR_{chk}=10\) dB and the target PSNR = 40 dB, in SoftCast, the bandwidth usage for texture and depth map videos with a single reference viewpoint is \(N_{sub}\) and \(N_{sub}\), respectively, and the achieved overall quality  
Figure 4: Power allocation for texture and depth map of 3D video sequences: (**a**) Kendo; (**b**) PoznanStreet.  
Figure 5: Bandwidth usage for texture and depth map of 3D video sequences: (**a**) Kendo; (**b**) PoznanStreet.  
of 3D video is 42.26 dB. However, we achieved a PSNR of 39.30 dB with the proposed target distortion algorithm using only \(167/N_{sub}\) = 8.15% and \(42/N_{sub}\) = 2% of the original bandwidth use for the texture and depth map. When the optimal PAR was applied, the proposed resource control scheme achieved a PSNR of 39.66 dB, close to the desired quality, while maintaining the same bandwidth usage.  
The bandwidth usage saving while achieving a target PSNR under a power budget \(SNR_{chk}\) = 20 dB is shown in Figure 7. As mentioned earlier, the bandwidth resource usage percentage will be used for the comparison between our proposed scheme and the resource-distortion algorithm [31]. In this figure, the total bandwidth usage percentage is split equally between the texture and depth map. The proposed resource control scheme achieves PSNR results closer to the target quality than the resource-distortion algorithm, with less bandwidth usage. For example, in Figure 7b, the target PSNR = 45 dB. For the resource-distortion algorithm, the bandwidth usage to achieve 43.379 dB was 149/512 = 7.25% for texture and the same for the depth map. In contrast, the proposed resource control scheme achieved a PSNR of 43.556 dB with only 558/2048 = 6.8% and 350/2048 = 4.25% bandwidth usage for the texture and depth map, respectively.