In this work, we introduce Blank Space Adaptive Causal Random Linear Network Coding (BS-AC-RLNC), a novel coding scheme designed to mitigate the triplet trade-off between throughput-delay-efficiency in multi-hop networks. BS-AC-RLNC leverages the physical limitations of the network, considering the bottleneck from each node to the destination. In particular, this approach introduces a light-computational re-encoding algorithm, called AC-RLNC (NET), implemented independently at intermediate nodes. NET adaptively adjusts the Forward Error Correction (FEC) rates and schedules idle periods. It incorporates two distinct suspension mechanisms: 1) Blank Space Period, accounting for the forward-channels bottleneck, and 2) No-New No-FEC approach, based on data availability. We present theoretical lower and upper bounds on in-order delivery delay, goodput, and throughput; in the case of in-order delay, we further derive a mean bound. These analytical results are extended to the multicast scenario, providing a broader understanding of the algorithm's performance under diverse network conditions. The experimental results achieve significant improvements in resource efficiency, demonstrating a 20% reduction in channel usage compared to baseline RLNC solutions. Notably, these efficiency gains are achieved while maintaining competitive throughput and delay performance, ensuring improved resource utilization does not compromise network performance.

翻译：本文提出了一种名为空白空间自适应因果随机线性网络编码（BS-AC-RLNC）的新型编码方案，旨在缓解多跳网络中吞吐量-时延-效率三者之间的权衡困境。BS-AC-RLNC充分利用网络的物理限制，综合考虑从每个节点到目的端的瓶颈。特别地，该方法在中继节点独立实现了一种轻量计算的重编码算法——AC-RLNC（NET）。NET自适应调整前向纠错（FEC）码率并安排空闲周期。它融合了两种不同的暂停机制：1) 空白空间周期，用于应对前向信道瓶颈；2) 基于数据可用性的无新数据则无FEC（No-New No-FEC）方法。我们给出了按序交付时延、有效吞吐量和吞吐量的理论下界与上界；针对按序时延，进一步推导了其均值界。这些分析结果扩展至多播场景，提供了算法在多样化网络条件下性能的更广泛理解。实验结果表明，资源效率显著提升：与基准RLNC方案相比，信道使用率降低了20%。值得注意的是，这些效率增益是在保持竞争性吞吐量和时延性能的同时实现的，确保资源利用率的提升不会损害网络性能。