## 4 Results  
To demonstrate our proposed framework, the H.264/AVC encoder was used to simulate an error-prone channel model by inserting errors at multiple points. The videos were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Encoding Complexity** & **Decoding Complexity** & **Remarks** \\ \hline \multirow{4}{*}{Polar Codes [19]} & \multirow{4}{*}{\(O(N\ log\ N)\)} & \multirow{4}{*}{\(O(N\ log\ N)\)} & Recursive nature can lead to increased latency and memory requirement; relatively higher computational overhead; implementation complexity for real-time video. \\ \cline{1-1} \cline{3-4} RL-AFEC based on RS codes [15] & \multirow{4}{*}{\(O(k\cdot(n-k))\)} & \multirow{4}{*}{\(O(k^{3})\)} & Efficient for small to moderate k; decoding can be intensive; reinforcement learning would add additional complexity. \\ \hline \multirow{4}{*}{QC-LDPC lattice codes [20]} & \multirow{4}{*}{\(O(e\cdot(n-k)\cdot k)\)} & Optimized encoding; decoding depends on iterations for convergence; potential security risks may arise from implementation flaws \\ \cline{1-1} \cline{3-4} LSTM based Adaptive FEC [21] & \multirow{4}{*}{\(O(k\cdot n\cdot T\cdot(h\cdot d+h^{2})\)} & \multirow{4}{*}{\(O  
first compressed by the H.264/AVC encoder, offering lossless compression to overcome bandwidth limitations. Then, selective encryption of syntax elements (sign bits of MVD and TC) and the proposed FEC algorithm were implemented in H.264/AVC's CABAC entropy coder during the encoding process. The decoding process was simulated using the H.264/AVC decoder, which decoded and corrected bit errors from the erroneous videos. The implementations were carried out on an HP Spectre x360 Intel Core i7 Processor with 16 GB RAM and a 64-bit operating system. Joint Scalable Video Model (JSVM) 9.19.14 was integrated in Visual Studio 2022 using C++ programming language to deploy the proposed method. As the aim of our work was to enhance the objective visual quality, the proposed scheme was applied on several test video sequences with different features, such as varying amounts of color pixels, texture, objects, and motion vectors. These results provided better visual quality when compared with the results obtained without using the FEC mechanism. The proposed method was not designed for fixed video resolution and can be applied to videos with different resolutions. In our work, we compared the results on video sequences of two different video resolutions. The results were evaluated on Common Intermediate Format (CIF) (352 \(\times\) 288) resolution on test video sequences MOBILE and FOOTBALL. For high-definition (HD) (1280 \(\times\) 720) resolution, the results were assessed using VIDYO1 and FOUR PEOPLE video sequences. These test video sequences are publicly accessible in Derf's collection. The frame rate was set to 30 fps, GOP size was 16, and subsampling was 4:2:0. H.264/AVC was used to encode and decode the CIF and HD test video sequences. The quality of the video sequences was evaluated through PSNR and PSNR\({}_{611}\) quality assessment metrics. Objective video quality can be measured using different evaluation parameters. Depending on the availability of the original video for the comparison, the evaluation methods are categorized as FR, RR, and NR assessment methods, as already described in Section 1. We used the FR technique to evaluate the video quality after recovery from errors by calculating the peak signal-to-noise ratio (PSNR) values. It is the ratio between the original video signal and the signal after passing through a processing scheme. Let y be the number of bits per frame and \((2^{y}-1)^{2}\) represents the range of values that a pixel can take, the PSNR is calculated as follows:  
\[PSNR=10\text{log}_{10}\,\frac{(2^{y}-1)^{2}}{MSE} \tag{12}\]  
PNSR is the most promising predictor used for evaluating video quality and it is dependent on the mean square error (MSE). MSE specifies the amount of similarity between the original video and the encoded/impaired video [45].