## 3 System Description
The received symbols can be expressed as follows:  
\[y_{ik}=\sqrt{G_{ik}}h_{il}x_{i}+n_{k} \tag{2}\]  
where \(G_{ik}\) and \(h_{ik}\) represent the path gain and the complex-valued channel gain, respectively, between transmitter \(i\) and receiver \(k\). Additionally, \(x_{i}\) denotes the transmitted symbol from \(i\), and \(n_{k}\) represents additive white Gaussian noise (AWGN) with a variance of \(N_{0}/2\). While more complex channel models (such as fading or shadowing models) could offer a more detailed representation of real-world conditions, the AWGN model provides a fundamental baseline to evaluate the effectiveness of our proposed techniques. This model simplifies the analysis, allowing for a focus on the impact of modulation and error correction strategies on the transmitted 3D video quality. According to [18; 19], if the path gain between nodes A and B is assumed to be one, then the path gain between the relay node R and either node A or B is given by:  
\[G_{Rk}=\left(\frac{\lambda_{AB}}{\lambda_{Rk}}\right)^{\beta} \tag{3}\]where \(\lambda\) denotes the distance between nodes, \(k\) represents either node A or B, and \(\beta\) is the path-loss exponent. In this study, we consider only the free-space path loss scenario (i.e., \(\beta=2\)), as used in [18,19]. After demodulation, the soft-bit codewords are decoded by the iterative network and IL-FEC decoder, as shown in Figure 4, which illustrates the decoder for node A. The same principle applies to the decoder for node B, so it is not separately depicted. The soft-bit streams \(L(\tilde{u}_{3})\), \(L(\tilde{u}_{34})\), \(L(\tilde{p}_{3})\), and \(L(\tilde{p}_{4})\) are directly received from node B, while  
Figure 3: A joint network and channel encoder in node R: (**a**) proposed, (**b**) reference 1, (**c**) reference 2. \(L(\tilde{u}_{1234})\), \(L(\hat{p}_{13})\), and \(L(\hat{p}_{24})\) are received from node R. The proposed network and IL-FEC decoder provide a priority information (API) before the first half-iteration by using the received soft-bit streams from node R and the soft-bit streams already available at node A (i.e., \(L(u_{12})\), \(L(p_{1})\), and \(L(p_{2})\)). This approach differs from the conventional IL-FEC decoding process shown in Figure 4. The initial APIs are extracted using the \(\boxplus\) operation, defined as \(L(\tilde{u}_{1234})\boxplus L(u_{12})\ \underline{\triangleq}\ L(\tilde{u}_{1234} \oplus u_{12})\)[20]. Through the extracted \(L(\tilde{u}_{34})\), \(L(\tilde{p}_{3})\), and \(L(\tilde{p}_{4})\), the directly received soft-bit streams \(L(\tilde{u}_{34})\), \(L(\tilde{p}_{3})\), and \(L(\hat{p}_{4})\) can be improved before the iterative decoding process begins. The same demodulation and decoding process applies to \(L(\hat{u}_{1})\) and \(L(\hat{u}_{2})\) at node B, so the details for node B are omitted. Once decoding is complete, \(\hat{u}_{3}\) and \(\hat{u}_{4}\) are decompressed into multi-view videos 3 and 4 and the corresponding depth maps. If node A wishes to view an intermediate virtual viewpoint that was not directly received, it can be created using the received MVD content in conjunction with the depth image-based rendering (DIBR) technique [21; 22; 23].