## 4 Performance Evaluation
### SSIM Performance  
In this subsection, we examine how the BER performances influence the SSIM performances shown in Figures 9 and 10, which correspond to the _Pantomine_ and _Champagne Tower_ sequences, respectively. The target SSIM value is set to 0.9, as this is generally considered to indicate good quality by human observers [26]. Since the final output displayed to viewers consists of color videos rather than depth videos, the evaluation focuses solely on the quality of the left and right color videos, as well as the virtually generated center color video.  
A key interest of this study is to ensure that all receivers obtain high-quality video, regardless of the relay node's location. To this end, the SSIM values obtained from each relay node position are averaged for each hierarchical value (\(\alpha\)). This approach means that the overall average SSIM can be impacted if the performance deteriorates at any specific relay position.  
Figure 7: The BER performances of the first referenced network and channel coding method.  
For the proposed method, the target SSIM is achieved for the reconstructed left and right view videos when \(\alpha\) is greater than 0.6, regardless of the test sequences used. In contrast, the first and seocond reference methods require \(\alpha\) to be greater than 1.4 and 0.8, respectively, to meet the target SSIM. Both the proposed and second reference methods, which use a low modulation order like 4 QAM, demonstrate improved SSIM performances across various \(\alpha\) values, regardless of relay location. However, when \(\alpha\) exceeds 1.4, all network and channel coding methods show saturated SSIM performances, irrespective of the relay position, indicating that the proposed method does not significantly enhance SSIM for the reconstructed left and right view videos in these conditions. In other words, the proposed method consistently outperformed the referenced methods in terms of BER and SSIM, especially for critical color data. However, the first referenced method showed advantages in scenarios where relay positioning was less central, indicating its suitability in environments with less ideal relay placements.  
Figure 8: The BER performances of the second referenced network and channel coding method.  
Figure 9: The SSIM performances of the _Pantomime_ test sequence.  
The virtual center view presents a different scenario, as shown in Figures 8(b) and 8(b). For the proposed method, the target SSIM is met when \(\alpha\) is set to 0.6. While the virtual view for the _Champagne Tower_ sequence slightly falls short of the target SSIM, this is not a major issue due to the complexity of the subject in this sequence. To illustrate this subjectively,  
Figure 10: The SSIM performances of the _Champagne Tower_ test sequence.  
Figure 11 presents four video frames. Figure 10(a) shows the original view (SSIM = 1), and Figure 10(b) shows a virtually generated center frame (SSIM = 0.9025), generated by VSRS under error-free conditions. For _Champagne Tower_, the target SSIM is not met even without transmission errors because the complexity of the champagne tower subject causes a reduction in synthesis quality. However, as shown in Figure 10(c), when the SSIM drops to about 0.85, the quality is nearly indistinguishable from the error-free case. If the SSIM drops further to 0.8, the degradation becomes visible to the naked eye.  
The key reason the proposed method with \(\alpha=0.6\) excels in performance for the virtually generated center view is that both the 2D color video streams and the associated depth map streams meet the target BERs simultaneously, regardless of relay location, as illustrated in Figure 6. In summary, our evaluation demonstrates that the proposed method significantly outperforms the referenced methods in terms of BER performance, particularly for color video streams. This directly translates into better video quality, as evidenced by the SSIM results.