## 2 Related Work  
A variety of solutions have been explored in the literature to mitigate video quality concerns over wireless networks. The integration of FEC techniques enables the receiver to detect and correct errors in real-time applications by embedding redundant bits into packets during transmission. It mitigates delay impacts, ensuring industrial wireless systems meet stringent latency and reliability requirements [19]. [PERSON] et al. [15] proposed a novel RL-AFEC approach based on frame-level Reed-Solomon (RS) codes that can learn to optimize FEC parameters in real time to minimize latency and packet loss by automatically adjusting the redundancy rate for each frame. In [17], the authors provide a survey on crypto-coding, joint encryption, and error correction techniques to simultaneously ensure the confidentiality and reliability of data transmission. They classify the joint schemes into three categories: joint encryption and channel coding (JECC), joint encryption and source coding (JESC), and joint encryption and network coding (JENC). JECC includes code-based encryption, lattice-based encryption, and hash-based encryption; JESC includes compressive sensing-based encryption and transform domain encryption; and JENC encompasses network coding-based encryption and secure network coding. Our proposed work is based on JESC transform domain encryption, which combines encryption and source coding to compress and encrypt data simultaneously using the H.264/AVC video codec. [PERSON] et al. [20] proposed a novel joint encryption, channel coding, and modulation scheme, called the quasi-cyclic low-density parity check (QC-LDPC) lattice code, to provide improved security, better error correction, and efficient transmission over wireless networks. Their approach implements lattice-based encryption to offer semantic security, QC-LDPC codes for robust error correction, and a lattice-based modulation scheme for robust transmission. However, it requires complex computations and secure key management, which we have focused on to optimize in our work. A novel method that adapts to changing channel conditions using deep learning was designed in [21]. This adaptive FEC approach was based on long short-term memory (LSTM) neural networks, employing frame-level RS coding to dynamically select appropriate redundancy levels to achieve higher VMAF scores, but it requires significant computational resources and a large training dataset to successfully implement, which may be difficult to obtain. A combination of subjective and objective video quality assessments was conducted in [22]. For subjective quality assessment, a user study with 40 participants was conducted, whereas various FR and NR quality metrics, including PSNR, SSIM, MS-SSIM, ST-RRED, FAST, and VMAF, were used for objective video quality assessment. The authors suggestedthe use of the H.264 codec over newer codecs such as HEVC, VP9, and AV1, as 91% of video streaming services utilize it and most browsers and devices do not provide full support for advanced standards. The joint-channel rate distortion (RD) optimization method presented in [23] minimizes end-to-end distortion of video signals and optimizes bitrate allocation among different video frames using a combination of RS and convolutional codes. Although it protects video signals against channel errors, it is not suitable for scalable high-resolution videos and involves complex computations. In [24; 25], the authors evaluated the performance of various video coding standards, including H.264/AVC, H.265, AV1, VP9, HEVC, and VVC, to check the similarity between original and compressed videos using a machine learning-based approach to predict optimal encoding parameters such as video sequence, resolution, and bitrate. Their work discussed several quality assessment metrics, such as PSNR\({}_{611}\), SSIM, and VMAF, and suggested the use of PSNR\({}_{611}\) objective video quality metrics over other methods.