Message-Level Scheduling for RLNC-Coded Multi-Source Traffic

2026-09-09

AI Summary

本文研究了多源RLNC编码消息流在有限处理能力下的加权解码延迟最小化问题，提出了一种基于消息权重和剩余解码差额的消息感知创新赤字调度算法（MAIDS）。

Abstract

This paper studies weighted decoding-delay minimization for multiple RLNC-coded message streams that compete for finite processing capacity at a destination. Packet arrivals are exogenous, while the scheduler only determines the processing order of packets already available at the destination. A trace-conditioned offline scheduling formulation shows that a batch-release subclass is strongly NP-hard even with a single processing unit. Message-Aware Innovation-Deficit Scheduling (MAIDS) is then developed to prioritize each serviceable message according to its weight and remaining decoding deficit. For a single processing unit, MAIDS is shown to be exactly optimal under nonblocking progressive arrivals with equal weights and under common activation with arbitrary positive weights, while the unrestricted weighted online problem admits no universal deterministic $O(1)$ competitive ratio. Simulation results on streaming and batch benchmarks show that MAIDS consistently reduces weighted decoding delay relative to the tested baselines, remains close to the offline optimum on average, and recovers the predicted exact performance boundaries.

Problem

• RLNC-coded
• multi-source traffic
• weighted decoding-delay
• finite processing capacity

Innovation

• Message-Aware Innovation-Deficit Scheduling (MAIDS)
• RLNC-coded messages
• weighted decoding delay minimization
• processing order optimization