###### Abstract
_Subjective video quality_ is the assessment of video quality from the end user's perspective. It is performed by asking a particular user their opinion about the quality of the video at the receiving end after passing through all of the processing stages. Subjective video quality assessment is highly dependent on the observer, the environment in which the observation is conducted, and the elements that are considered to deduce the results by the observer (user preferences in terms of color, brightness, display size, or resolution). _Objective video quality_ is the measure of quality degradation of the video as it goes through a number of processes, including encoding, compression, and transmission. Calculating the mean square error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and video multimethod assessment fusion (VMAF) is a commonly used mathematical method to predict video quality [1; 2]. Objective video quality evaluation techniques can be categorized as full reference (FR), reduced reference (RR), or no reference (NR) depending on the amount of information from the original video being used for comparison [3]. FR techniques compare each and every pixel of the original and impaired videos, unaware of the processes applied in between. RR procedures use some of the characteristics from both of the videos for comparison. NR methods attempt to recover the impaired video without knowing anything about the original video [4; 5; 6]. Error correcting codes (ECC) are used while encoding the data before transmission to identify any change that may occur during transmission. ECC for video applications can be categorized into four different mechanisms: forward error correction (FEC), retransmission, error resilience (ER), and error concealment (EC) [7]. A hierarchical representation of error controlling schemes is shown in Figure 1. FEC techniques add redundant bits to the original bitstream for error recovery. FEC can be carried out in two ways; block coding and convolution coding. Block codes work on the data in fixed-sized blocks, whereas convolution codes work on bitstreams of arbitrary size. In retransmission techniques, the receiving device sends an acknowledged signal to the transmitting device to acknowledge the received or lost packet and requests the sender to retransmit the data. It is applicable only when the number of errors is small, e.g., the automatic repeat request (ARQ) needs a backchannel between two communicating devices and takes additional time to retransmit the data, which makes it unsuitable for interactive real-time video transmission, broadcast, unicast, and multicast applications [8; 9]. ER methods are used to prevent error propagation in a bitstream during transmission. EC provides methods for hiding the effects of errors or packet loss and presents the visual information in such a way that makes those errors unnoticeable by the user [10]. Spatial interpolation regenerates the missing data in intra-coded frames using neighboring pixels, while temporal interpolation is used to reconstruct lost data in inter-coded frames using other reference frames [11; 12]. An FEC technique is used in our work to recover errors that occur during transmission to avoid the need to retransmit the data. These errors may affect the quality, visibility, and completeness of information by causing blurriness, color alteration, false edges, jagged motion, flickering, and chrominance in the video sequence [13]. It makes broadcasting, video streaming, and real-time applications more efficient. FEC is generally used for the transmission of video, audio, and other signals for which a transmission backchannel is not available and retransmission is not possible in case of error occurrence. Moreover, the huge size of video files will take a lot more time if retransmitted. So, FEC provides a better and time-saving mechanism to cover up the damage caused by these errors [14].