METTLE: Efficient Streaming Erasure Code with Peeling Decodability

2026-02-10

AI Summary

This work proposes METTLE (Multi-Edge Type with Touch-less Leading Edge), a novel streaming erasure code designed to overcome the longstanding trade-off among coding efficiency, computational complexity, and decoding latency in networks and distributed systems. By leveraging a multi-edge-type graph structure combined with a touch-less leading edge design, METTLE achieves near-optimal coding efficiency while simultaneously enabling extremely low decoding latency and complexity, along with support for efficient peeling decoding. Experimental results demonstrate that, under small source block configurations, METTLE accelerates decoding by 47.7 to 84.6 times compared to streaming RaptorQ, with only a marginal reduction in coding efficiency—thereby breaking the conventional trilemma that has constrained prior approaches.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In this work, we solve a long-standing open problem in coding theory with broad applications in networking and systems: designing an erasure code that simultaneously satisfies three requirements: (1) high coding efficiency, (2) low coding complexity, and (3) being a streaming code (defined as one with low decoding latency). We propose METTLE (Multi-Edge Type with Touch-less Leading Edge), the first erasure code to meet all three requirements. Compared to"streaming RaptorQ"(RaptorQ configured with a small source block size to ensure a low decoding latency), METTLE is only slightly worse in coding efficiency, but 47.7 to 84.6 times faster to decode.

Problem

• erasure code
• coding efficiency
• coding complexity
• streaming code
• decoding latency

Innovation

• erasure code
• streaming code
• peeling decoding
• low decoding latency
• coding efficiency