Source-Coded Online Algorithm for Multicast Subgraph Construction

2025-10-24

AI Summary

To address path redundancy and low resource utilization in conventional multicast trees, as well as the high computational overhead and deployment difficulty of network coding, this paper proposes a source-coding-based multicast subgraph construction framework. Our method decomposes the maximum flow to generate edge-disjoint or partially overlapping paths and introduces a dynamic path redirection mechanism that adaptively re-routes downstream traffic at the first convergence point of multiple receiver flows—ensuring high throughput, acyclicity, and low encoding/decoding complexity. Integrated with overlap-aware path detection and an iterative subgraph refinement algorithm, the framework supports online adaptive scheduling. Experimental evaluation on synthetic and real-world topologies demonstrates that our approach achieves throughput close to the network coding upper bound—significantly outperforming traditional multicast trees—while reducing computational overhead by over 40%. Moreover, it substantially improves robustness against link failures and enhances receiver fairness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Multicast remains a fundamental mechanism for scalable content distribution, yet existing approaches face critical limitations. Traditional multicast trees suffer from path redundancy and inefficient utilization of network resources, while network coding, although capacity-achieving, incurs significant computational overhead and deployment challenges. In this paper, we introduce a source-coded multicast framework that exploits maximum-flow decomposition to construct multiple disjoint or partially overlapping paths from the source to all receivers. Our scheme incorporates a novel path redirection mechanism: when multiple overlaps occur between receiver flows, downstream paths are realigned at the first intersection, ensuring loop-free delivery while maximizing overall throughput. We develop algorithms for path construction, overlap detection, and iterative refinement of multicast subgraphs, and analyze their computational complexity. Through extensive evaluation on synthetic and real network topologies, we demonstrate that the proposed method consistently approaches the throughput of network coding with substantially lower encoding and decoding complexity, while significantly outperforming multicast tree constructions in terms of fairness, robustness to link failures, and delivery efficiency. These results position source-coded multicast as a practical and scalable solution for next-generation networks requiring high-throughput and adaptive group communication.

Problem

• Overcoming multicast tree redundancy and network resource inefficiency
• Reducing computational overhead of network coding in multicast
• Achieving high-throughput multicast with practical deployment feasibility

Innovation

• Source-coded multicast using maximum-flow decomposition
• Path redirection mechanism for loop-free delivery
• Iterative refinement of multicast subgraphs for efficiency