Adaptive network coding schemes provide a promising approach to bridging the gap between high data rates and low delay in real-time streaming applications. However, their effectiveness often relies on accurate channel prediction, which is typically based on delayed feedback and is especially challenging when the underlying channel model is unknown. To address this, we introduce a novel integration of network coding with a channel-agnostic, Deep learning-based Noise Prediction algorithm (DeepNP). Unlike traditional estimators, DeepNP predicts statistical noise rates rather than instantaneous noise realizations, significantly simplifying the prediction task while enhancing coding performance. DeepNP is designed to operate with both binary (e.g., acknowledgments) and continuous-valued (e.g., Signal-to-Noise Ratio, SNR) feedback. We incorporate DeepNP into the Adaptive and Causal Random Linear Network Coding (AC-RLNC) framework to jointly optimize throughput and in-order delivery delay. Two variants are proposed: (i) Erasure-Rate DeepNP (ER-DeepNP), which serves as a transport-layer noise predictor and achieves in a numerical study up to a 2x reduction in mean and maximum delay with less than 0.1 loss in throughput compared to statistic-based estimators, under Round-Trip Time (RTT) up to 40 time slots and erasure rates up to 60%; and (ii) Cross-Layer DeepNP (CL-DeepNP), which dynamically adjusts the SNR threshold to maintain high physical layer code rates while achieving low transport-layer erasure rates. This yields, in the presented numerical study, a 25% throughput gain over fixed-threshold approaches. Our results demonstrate that DeepNP enables robust, model-free noise prediction, making adaptive network coding more viable in practical, feedback-limited communication scenarios.

翻译：自适应网络编码方案为实时流应用在高速率与低延迟之间架起了一座桥梁。然而，其有效性通常依赖于基于延迟反馈的信道预测，且当底层信道模型未知时该预测尤为困难。针对这一问题，我们提出了一种将网络编码与信道无关的深度学习噪声预测算法（DeepNP）的创新融合方案。与传统估计器不同，DeepNP预测统计噪声率而非瞬时噪声实现，显著简化了预测任务同时提升了编码性能。DeepNP可同时处理二元反馈（如确认信号）和连续值反馈（如信噪比SNR）。我们将DeepNP融入自适应因果随机线性网络编码（AC-RLNC）框架，以联合优化吞吐量和有序交付延迟。本文提出两种变体：（i）擦除率DeepNP（ER-DeepNP），作为传输层噪声预测器，在数值研究中相比基于统计的估计器，在往返时延（RTT）高达40个时隙、擦除率高达60%的条件下，实现了平均延迟和最大延迟降低2倍且吞吐量损失小于0.1；（ii）跨层DeepNP（CL-DeepNP），通过动态调整SNR阈值以维持高层物理层码率，同时实现低传输层擦除率。数值研究表明，相比固定阈值方法，该方案可获得25%的吞吐量增益。实验结果表明，DeepNP实现了鲁棒的、无模型的噪声预测，使自适应网络编码在受反馈限制的实际通信场景中更具可行性。