Low Earth Orbit (LEO) satellite networks are expected to support global multimedia services in future sixth-generation (6G) systems, where streaming media constitutes a major traffic type with stringent Quality of Experience (QoE) requirements. However, existing satellite resource allocation schemes mainly optimize Quality of Service (QoS) metrics, overlooking streaming-specific characteristics such as playback buffering and user early departure, which may lead to severe QoE degradation and on-board energy waste. To address this issue, this paper proposes a QoE-oriented downlink power allocation scheme for on-demand streaming services in LEO satellite networks. We first formulate a long-term stochastic optimization problem that jointly minimizes playback stalling and energy waste caused by buffer overflow and user interruption, while guaranteeing long-term user QoE. By leveraging Lyapunov optimization, the original problem is transformed into deterministic per-slot subproblems. A low-complexity power allocation algorithm is then developed based on strong subgradient theory, with theoretical convergence guarantees established through the strong quasiconvexity analysis of the per-user objective function. Simulation results based on the practical OneWeb constellation demonstrate that the proposed algorithm substantially improves user Mean Opinion Score (MOS) while significantly reducing on-board energy waste. In particular, a microscopic analysis of per-user power dynamics reveals that the performance degradation of conventional QoS-centric schemes stems from their buffer-agnostic, channel-driven allocation, which causes severe resource imbalance and energy waste—an inherent limitation that the proposed scheme effectively circumvents through application-layer awareness.
Huazhi Feng, Feng Wang, Junyu Lai et al.· IEEE Open Journal of the Com...· 0 citations
Integrated satellite–aerial networks (ISANs) are emerging as a promising architecture that combines high-throughput inter-satellite transmission with the agility of uncrewed aerial vehicles (UAVs) to support flexible and low-latency traffic delivery. Owing to the inherently uneven traffic distribution in the satellite layer, traffic flows often suffer from congestion and excessive multi-hop forwarding delays. UAVs can act as adaptive relays to offload congested traffic and mitigate routing detours, thereby reducing end-to-end latency. However, latency-aware traffic management in ISANs is fundamentally challenged by highly dynamic satellite topologies, heterogeneous link characteristics, and the tight coupling between satellite traffic dynamics and UAV mobility. Existing approaches often suffer from cross-layer misalignment between satellite routing and aerial relaying, which limits coordinated latency adaptation. To address these challenges, this paper proposes an agentic UAV-assisted relay framework, termed DUS-SACUD, in which an autonomous UAV acts as an embodied agent that proactively steers traffic. First, a graph-conditioned diffusion model is developed for generative UAV–satellite link (USL) selection under dynamic network states. Second, a soft actor–critic-based reinforcement learning scheme is employed for embodied UAV deployment to minimize USL-induced delay. Through closed-loop alternating execution, DUS-SACUD jointly optimizes connectivity adaptation and mobility control in ISANs. Extensive simulations based on a realistic satellite constellation demonstrate significant end-to-end latency reduction over existing routing and UAV-assisted baselines, while maintaining robust performance under diverse ISAN conditions.
Xintong Li, Feng Wang, Qi Wu et al.· IEEE Transactions on Cogniti...· 0 citations
In resource-constrained low Earth orbit satellite-terrestrial cooperative Internet of Things (L-STCIoT), overloaded user demand poses severe challenges to underlying network performance optimization. To address the resource allocation problem in this scenario, a joint resource allocation framework is proposed, enabling the satellite to make coordinated decisions on user selection and transmission resource allocation based on user demands. In this framework, satellite resource cost and user-side satisfaction are defined as quantifiable metrics, respectively, and are jointly modeled to maximize the system’s weighted profit. Due to the nonconvexity of the original problem and strong variable coupling, it is decomposed into two subproblems: user selection and transmission resource allocation. Specifically, the former is addressed using a relaxation-based minorize–maximization (MM) algorithm combined with a discrete mapping (DM) mechanism, where a strongly concave surrogate function is constructed to improve decision efficiency and stability. The latter is reformulated as a linear programming (LP) problem via variable decoupling and solved using the interior-point method, thereby reducing computational complexity. In the performance analysis section, the convergence of the proposed joint optimization algorithm is proved based on the Lyapunov convergence framework, demonstrating that the performance loss introduced by the DM mechanism is bounded and that the final solution obtained by the algorithm is a globally approximate optimal solution to the original optimization problem. Simulation results demonstrate that the method proposed in this article effectively balances satellite resource cost and user-side satisfaction under different system scales and user demand distributions, while significantly improving the overall system profit, thereby exhibiting superior performance.
Yi Zheng, Chengjie Li, Lidong Zhu et al.· IEEE Internet of Things Jour...· 0 citations
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