An efficient Sheep Flock Optimization-based hybrid deep RaNN for secure and enhanced video transmission quality

Video communication gained wide popularity since it offers a more real-time mode of face-to-face interaction. This paper mainly aims to enhance the video transmission quality of multimedia applications via communication network security improvement and an adaptive resource allocation strategy. The main aim of the resource allocation strategy is to achieve efficient usage of spectrum resources since video communication utilizes the orthogonal subcarriers for data transmission and the need for conversion of non-flat channels to flat. An optimized Hybrid Deep Random Neural Network (HDRaNN) architecture is used to overcome the adaptive spectrum resource allocation problem in the single-frequency network transmission by taking into account the different time-frequency resources and regions for overlapping areas and the same time-frequency for non-overlapping areas. The Sheep Flock Optimization algorithm is used to minimize the network error rate of the HDRaNN architecture by optimizing the weights and biases. The basic purpose of communication network security in this paper is to ensure that data communicated over a network connection are safe and secure. The network security protocol offers protection from unauthorized data retrieval and attempts to obtain data. The protocols utilized for secure communication in the proposed technique are Secure Hypertext Transfer Protocol, Secure Socket Layer, Simple Network Management Protocol, and Secure File Transfer Protocol. This protocol also assures that unauthorized users, devices, or services do not have access to your network data and works on all network mediums and data types. The extensive experiments conducted using the proposed methodology show improvements in the video transmission quality in terms of high diversity gain, security, increased throughput, and minimized bit error rate.