Blank Space: Adaptive Causal Coding for Streaming Communications Over Multi-Hop Networks

2025-02-17

AI Summary

To address the inherent three-way trade-off among throughput, end-to-end latency, and resource efficiency in multi-hop networked streaming, this paper proposes an Adaptive Causal Random Linear Network Coding (AC-RLNC) framework. The method introduces two key innovations: (1) Blank Space periodic scheduling and (2) a “No-New No-FEC” dual suspension mechanism—enabling lightweight, node-autonomous causal-constrained recoding that alleviates channel bottlenecks while preserving data availability. The framework integrates dynamic FEC rate adaptation, idle-period-aware scheduling, and a low-overhead recoding algorithm (NET). Experimental results demonstrate that, compared to standard RLNC baselines, AC-RLNC reduces channel occupancy by 20%, maintains comparable throughput and end-to-end latency, and significantly improves spectral and computational resource utilization efficiency.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Agent Communication
• Machine Learning: Causal Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In this work, we introduce Blank Space AC-RLNC (BS), a novel Adaptive and Causal Network Coding (AC-RLNC) solution designed to mitigate the triplet trade-off between throughput-delay-efficiency in multi-hop networks. BS leverages the network's physical limitations considering the bottleneck from each node to the destination. In particular, BS introduces a light-computational re-encoding algorithm, called Network 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. 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.

Problem

• Mitigate throughput-delay-efficiency trade-off
• Adaptive Forward Error Correction rates
• Reduce channel usage by 20%

Innovation

• Adaptive Causal Network Coding
• Light-computational re-encoding algorithm
• Blank Space Period scheduling