In the large-scale dynamic Low Earth Orbit (LEO) satellite networks, the conventional TCP-based single-path transmission encounters challenges such as prolonged propagation delay, frequent connection failures, and suboptimal resource utilization. In this paper, we propose an Integrated Multi-Path Network Coding (IMPNC) transmission scheme. This scheme leverages multiple paths for end-to-end transmission to achieve bandwidth aggregation and redundant backup. The multi-path transmission is facilitated by Multi-Path Quick UDP Internet Connection (MPQUIC) protocol to adapt to the limited satellite bandwidth and caching resources. The proposed approach involves encoding packets at nodes along the paths, addressing the significant out-of-order problem arising from variable delays on different paths. Additionally, we present a Software Defined Networking (SDN)-based domain clustering architecture, which offers a more streamlined control approach, reducing overall complexity. Furthermore, we formulate the domain clustering problems as mixed-integer nonlinear programming and the coding-based routing problem as a Steiner tree problem. Evaluation results demonstrate that the proposed scheme effectively reduces the latency over 25.1%, enhances bandwidth utilization by 19.6%, and ensures reliable data transmission by reducing retransmission probability by 4.1%.

Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

The natural mesh network topology of satellite networks makes multi-path transmission prevalent due to its ability to provide bandwidth aggregation and backup using redundant paths. While as one of the significant benefits of multi-path transmission, high reliability requires extensive signaling to achieve excellent performance. So designing an analysis and treatment method to tackle the network overhead and reliability balance remains a major challenge. In addition, massive and burst services in satellite networks will have different demands, which require the network to execute fine-grained path scheduling and management of the multi-path transmission. In this paper, we carefully model the multi-path reliability and network overhead factors under the SDN-based integrated satellite-terrestrial network architecture to characterize the network state. Second, an adaptive multi-path selection scheme is designed to handle the number of active paths, which considers different Quality of Service (QoS) requirements under the limited resources of satellite networks. Then, we formulate the problem as Non-Linear Binary Programming (NLBP) and develop a practical Particle Swarm Optimization (PSO)-based algorithm to solve it. Simulation results show that the proposed scheme can improve satellite networks’ throughput and resource utilization.