DSROQ: Dynamic Scheduling and Routing for QoE Management in LEO Satellite Networks

2025-08-28

AI Summary

To address heterogeneous Quality-of-Service (QoS) requirements of diverse applications in Low-Earth Orbit (LEO) satellite networks, this paper proposes a joint optimization framework for routing, bandwidth allocation, and dynamic queue scheduling. The method models QoS constraints as tunable soft constraints and introduces an adaptive cooperative optimization algorithm based on Monte Carlo Tree Search (MCTS), integrated with Lyapunov optimization for time-varying scheduling reward evaluation. This approach effectively tackles the NP-hard nature of the joint decision problem. Experimental evaluation on a Starlink constellation model demonstrates that the proposed method improves user Quality-of-Experience (QoE) by 23.7% and fairness index by 19.4% over baseline schemes, with particularly pronounced gains in bandwidth-sensitive scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

Problem

• Optimizing routing and bandwidth allocation in LEO satellite networks
• Managing diverse QoS requirements for different applications
• Solving NP-hard joint optimization problem with adaptive scheduling

Innovation

• MCTS-inspired routing and bandwidth allocation method
• Lyapunov optimization-based dynamic scheduling approach
• Joint optimization treating QoS as soft constraints