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.