AI-based quality of service optimization for multimedia transmission on Internet of Vehicles (IoV) systems

Abstract

Multimedia Communications of Internet of Vehicles (IoV) uses WLAN, NFC and Fifth Generation networks. At the same time, in multimedia communications in healthcare, IoV's essential task is optimizing the quality of experience (QoE) via regulating wireless links between cars. In addition, the artificial intelligence (AI) method has revolutionized IoV's environment in total, and portable wireless devices have become highly essential to end consumers in their various activities for transferring multimedia material into IoV systems. Most consumers face their irritated and not-so-sufficient view of the performance, QoE. When the service delivery is not pleasurable, most customers can stop, and the market can eventually devalue the overall performance of a product, organization, or system as a whole. This article initially offers two new algorithms called Energy-aware QoE Optimization Algorithm (EQOA) and Queue aware QoE Optimization Algorithm (QQOA) and contrasts their results with Baseline. This article provides an alternative approach to these problems. Secondly, it presents a system for multimodal communication. Thirdly, multimedia IoV transmission through mobile devices offers the QoE Optimization Model. The experimental findings show that the proposed methods maximize QoE by delighting end-users service of mobile devices to levels greater than Baseline reference. Therefore, the suggested algorithms surpass the Baseline such that during multimedia transmission, they can be regarded as promising contenders for IoV implementations.