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

## TL;DR

This paper introduces DSROQ, a novel adaptive scheduling and routing algorithm for LEO satellite networks that optimizes QoE by jointly managing routing, bandwidth, and scheduling, outperforming benchmark schemes.

## Contribution

It presents a joint routing and bandwidth allocation framework with a Monte Carlo tree search-based solution tailored for LEO satellite networks, incorporating QoS as soft constraints.

## Key findings

- DSROQ improves user experience and fairness over benchmarks.
- Joint optimization outperforms separate routing and scheduling.
- Performance factors shift with traffic sensitivity.

## 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.