Abstract
With the extensive growth of the web as well as cellular networks, secure multimedia transmission through cellular networks is needed. Currently, fifth-generation (5G) cellular networks are utilized to perform secure multimedia transmission. Numerous studies have been conducted to design efficient resource allocation approaches for secure video transmission in 5G cellular networks. However, this approach does not offer complete video security related to security against dynamic eavesdroppers or patent defilements. Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. Here, the recorded educational videos are considered and are transmitted over 5G network transmission resources initially. The resources in 5G networks are allocated via the TDWO model by considering fitness parameters such as the data rate, achievable data rate, and quality of experience (QoE). Here, the deep convolutional neural network (DCNN) model is deployed for the prediction of the QoE in resource allocation. Moreover, extensive experiments are performed to identify the resource allocation performance of the designed TDWO model. The experimental results prove that the TDWO resource allocation algorithm yields significant experimental outcomes, with throughput, bit error rate (BER), QoE and fitness values of 25.557 Mbps, 0.021, 18.332 and 0.013, respectively.