# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 3 Source Coding Protection
### Evaluation of Source Coding Protection
For 1% and 3% TLR, the most robust encoding modes are LPI4 and IPPP. At 5% TLR, these two modes and IPIP show very similar performance. In addition, at 7% TLR and above, IPIP is the most resilient mode. At 20% TLR, AI mode is over IPIP for 4 tiles per frame layout and above, but very low PSNR values are obtained at this point, being not effective at this TLR values. From these experiments, it seems that the 1 tile per frame is the most robust layout. However, in the \"real world\", a particular PLR may produce very different TLRs, depending on the ratio between the tile size and the network Maximum Transfer Unit (MTU). As the tile size depends on several factors such as the frame type (I, P), the number of tiles per frame, and the selected QP parameter (the compression ratio), we will now evaluate the robustness of the combination of these parameters, for different PLR values. First of all, note that, if a tile is larger than the network MTU, that tile has to be divided into several network packets, and, if one of these packets gets lost, then the rest of the packets belonging to the same tile are completely useless because that tile cannot be decoded at all. The average number of network packets per tile in the encoded bit streams is shown in Table 3. For the 1 tile per frame layout, the proportion is over 4 packets per tile. This means that the loss of one network packet will probably entail the effective loss of four packets. For the 10 tiles per frame layout, the proportion is near 1 packet per tile. This means that the loss of one network packet will probably entail the effective loss of only that packet. Figure 4: PSNR for all the til/frm layouts and all the encoding modes with 3% and 10% of TLR. Non-FEC bit streams. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Packets Per Tile** & **1 til** & **2 til** & **4 til** & **6 til** & **8 til** & **10 til** \\ \hline AI & 4.38 & 2.52 & 1.37 & 1.17 & 1.12 & 1.09 \\ IPIP & 4.37 & 2.35 & 1.57 & 1.27 & 1.14 & 1.10 \\ IPPP & 4.40 & 2.46 & 1.49 & 1.27 & 1.17 & 1.10 \\ IPx & 4.18 & 2.38 & 1.41 & 1.14 & 1.05 & 1.02 \\ IPx25 pctCTU & 4.27 & 2.41 & 1.41 & 1.13 & 1.04 & 1.02 \\ IPx25 pctTIL & 4.26 & 2.42 & 1.43 & 1.20 & 1.08 & 1.07 \\ IPxpattern & 4.19 & 2.39 & 1.42 & 1.19 & 1.09 & 1.06 \\ LP & 4.30 & 2.43 & 1.45 & 1.20 & 1.10 & 1.06 \\ LPI4 & 4.34 & 2.35 & 1.59 & 1.35 & 1.23 & 1.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average number of Packets Per Tile for every encoding mode. Figure 5 shows the TLRs obtained for different PLRs, for both AI and LPI4 modes. It can be seen that the 1 tile per frame layout suffers from vulnerability in both modes, so this layout which apparently showed to be the most robust in the previous tests, now provides higher TLRs than the rest of the layouts, for a fixed PLR, which is a drawback. The obtained TLRs are strongly correlated with the values from Table 3, so when the number of packets per tile tends to one, the TLR obtains its minimum value. Dividing a frame into 6, 8 or 10 tiles per frame produces similar TLRs, because the proportions of packets per tile for these three modes are very close. Now, we will show the video quality performance of the different tiles per frame layouts, at a fixed PLR. In Figure 6, the PSNR values for each number of tiles per frame layout at 3% and 10% PLR, is shown. Again, LP, IPx, and IPx25 pctCTU obtain very low PSNR values, showing little resilience against errors. The other six modes have increasing PSNR values when the number of tiles per frame increases. This result is consistent throughout all the PLRs tested, therefore, in all the experiments, we have observed that when a high number of tiles per frame is selected, the recovered video sequence obtains a better PSNR value. For low packet loss (1%), IPPP and LPI4 are still the best encoding methods; for a medium packet loss percentage (3%), IPPP and IPIP are the best methods; and, for the rest of PLRs (5%, 7%, 10% and 20%), the best methods are AI and IPIP, although for values of 7% and higher, the PSNR values are very low.