Rateless Deep Joint Source-Channel Coding for Task-Oriented Image Communications

The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing a rateless deep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.