A normative algorithm for wireless multimedia broadcast retransmission using generative adversarial network coding

Retransmitting multimedia data files of varying lengths in wireless networks often suffers from inefficient coding, resulting in frequent retransmissions and reduced quality of service (QoS). Existing retransmission methods face two major limitations: traditional sequential splicing approaches struggle with overlapping packet losses in variable-length scenarios, and packet-length headers introduce substantial transmission overhead that degrades network performance. This study introduces BRAGNC, a wireless multimedia broadcast retransmission algorithm based on deep convolutional generative adversarial network (DCGAN) coding. BRAGNC addresses these limitations through three key mechanisms: a segment-by-segment approach with enhanced flag registers to minimize data processing time, adaptive packet splicing based on node feedback to improve retransmission efficiency, and flag bit delimiters to reduce header overhead. Experiments show that BRAGNC reduces retransmission frequency by 25%, improves coding efficiency by 15%, and decreases network overhead by 20% across diverse packet loss scenarios. These results provide a basis for advancing generative network coding in multimedia broadcasting. An algorithm for wireless multimedia broadcast retransmission reduces repeat transmissions in wireless multimedia networks by up to 25%. Deep learning techniques improve data coding efficiency while maintaining network quality. Adaptive packet splicing decreases transmission costs by 20% compared to existing methods.