Delay Sensitive Music Transmission Architecture for 5G VANET: Integrated Network Slicing and Predictive Beamforming

The rapid development of 5G-enabled vehicular ad hoc network (VANET) has opened new possibilities for real-time multimedia services, including music streaming in intelligent transmission systems. However, maintaining seamless, low-latency music transmission in high-mobility environments remains an issue. Traditional VANE suffer from improved packet loss, jitter, and transmission delays due to unpredictable vehicular movement, fluctuating network conditions, and inefficient resource allocation. It proposes a delay-sensitive music transmission architecture for 5G VANET by combining network slicing (NS) and predictive beamforming to increase real-time streaming efficiency. The primary goal is to decrease transmission latency, increase signal stability, and optimize resource allocation for seamless music playback in a dynamic vehicular environment. The proposed architecture utilizes a twofold approach such as NS allocating a dedicated ultra-reliable low-latency communication (URLLC) slice for music transmission, with a quality of service (QoS)-aware manager adjusting parameters to ensure low latency. Secondly, the adaptive radial movement optimized intelligent long short-term memory network- (ARMO-IntelliLSTM) based deep learning (DL) model predicts vehicle trajectories, enabling the system to preadjust beamforming parameters for continuous signal stability. The multiple-input, multiple-output- (mMIMO) based beamforming module dynamically adapts beam angle and handoff decisions on real-time channel state information. Simulation results demonstrate the effectiveness of the proposed architecture in reducing latency (3 ms), jitter (2.7 ms), and in increasing packet delivery ratio (PDR) (98.3%), beamforming accuracy (95%), handoff success rate (98.5%), and throughput (110 Mbps). Finally, integrating NS and predictive beamforming offers a robust solution for delay-sensitive music transmission in 5G VANET.