Optimization of High-Definition Music Streaming VANET Broadcast Protocol for QoS

The high-speed and the growing popularity of vehicular ad hoc networks (VANETs) and the ever-growing need to carry high-quality Multimedia Services, especially high-definition (HD) streaming music, have extensively compounded the pressure on efficient communication mechanisms. Established VANET broadcast protocols can hardly allow the low latency, high data transmission rates, and low packet loss demanded by continuous music streaming. In order to overcome these challenges, an optimized VANET broadcast protocol was deployed, which includes machine learning and superior optimization algorithms to increase the quality of service (QoS). A predictive Dynamic Transient Search Optimizer-driven Categorical Boosting (DTS-CatBoost) model is introduced to anticipate network congestion by analyzing traffic patterns, enabling proactive transmission adjustments. For network congestion control and routing optimization, DTS is employed to dynamically select the most stable broadcast nodes, optimizing data dissemination paths. Furthermore, the protocol leverages forward error correction (FEC), which is integrated to enhance data reliability in high-mobility scenarios. The proposed method is implemented using Python 3.10.1. Key performance metrics include packet delivery ratio (PDR), end-to-end latency, bandwidth utilization, protocol overhead, and playback smoothness. Experimental results demonstrate that the suggested model DTS-CatBoost significantly improves QoS, reducing playback interruptions, enhancing data transmission efficiency, and ensuring seamless HD music streaming in vehicular networks. It highlights the potential use of AI-driven adaptive streaming algorithms in transforming multimedia transmission across VANETs, paving the way for scalable and reliable streaming solutions in next-generation intelligent transportation systems.