In recent years, numerous data-intensive broadcasting applications have emerged at the wireless edge, calling for a flexible tradeoff between distortion, transmission rate, and processing complexity. While deep learning-based joint source-channel coding (DeepJSCC) has been identified as a potential solution to data-intensive communications, most of these schemes are confined to worst-case solutions, lack adaptive complexity, and are inefficient in broadcast settings. To overcome these limitations, this paper introduces nonlinear transform rateless source-channel coding (NTRSCC), a variable-length JSCC framework for broadcast channels based on rateless codes. In particular, we integrate learned source transformations with physical-layer LT codes, develop unequal protection schemes that exploit decoder side information, and devise approximations to enable end-to-end optimization of rateless parameters. Our framework enables heterogeneous receivers to adaptively adjust their received number of rateless symbols and decoding iterations in belief propagation, thereby achieving a controllable tradeoff between distortion, rate, and decoding complexity. Simulation results demonstrate that the proposed method enhances image broadcast quality under stringent communication and processing budgets over heterogeneous edge devices.

翻译：近年来，无线边缘涌现出众多数据密集型广播应用，要求实现失真、传输速率与处理复杂度之间的灵活权衡。虽然基于深度学习的联合信源信道编码（DeepJSCC）已被视为数据密集型通信的潜在解决方案，但现有方案大多局限于最坏情况设计、缺乏自适应复杂度且在广播场景中效率低下。为克服这些限制，本文提出非线性变换无速率信源信道编码（NTRSCC）——一种基于无速率码的广播信道变长联合信源信道编码框架。具体而言，我们融合学习型信源变换与物理层LT码，开发利用解码器侧信息的不等保护方案，并设计近似方法实现无速率参数的端到端优化。该框架使异构接收器能够自适应调整接收的无速率符号数量及置信传播解码迭代次数，从而在失真、速率与解码复杂度之间实现可控权衡。仿真结果表明，所提方法在异构边缘设备的严苛通信与处理预算下能够提升图像广播质量。