The dynamic nature of Low Earth Orbit (LEO) satellite networks introduces unique challenges to satellite edge computing (SEC), particularly regarding handover (HO) strategies for computational task continuity. Conventional HO strategies fail to account for computational task urgency, leading to task timeouts and reduced performance. This paper presents a Risk Predictive Proactive Handover (RPP-HO) mechanism tailored for SEC environments. Unlike conventional reactive approaches, RPP-HO enables satellites to proactively initiate handovers by predicting task timeout risks based on queue congestion and deadline constraints. A multi-step methodology incorporating task-level risk factor evaluation, greedy user selection, and adaptive locking mechanisms ensures task success without excessive handover frequency. Simulation results using real Starlink constellation data demonstrate that RPP-HO improves task success rates by effectively offloading at-risk tasks while maintaining system stability under varying workloads. The proposed strategy offers a lightweight, computation-aware enhancement to standardized Conditional Handover procedures in 6G Non-Terrestrial Networks (NTN).

This paper addresses the rate control issue for Video-on-Demand (VoD) services in Low Earth Orbit (LEO) satellite Internet. LEO systems employ long-distance Non-Orthogonal Multiple Access (NOMA), where the transmission rate of the last hop directly determines the Quality of Experience (QoE) levels for the VoD users and the satellite’s energy consumption. Our research identifies two primary issues: (i) determining the transmission rate to ensure high user QoE while minimizing energy consumption, and (ii) ensuring fairness among users within the satellite coverage area. To address these issues, we model the multi-user VoD viewing process as a Partially Observable Markov Process (POMDP) and describe the interactions among users using a cooperative coalition game framework. We propose Harmony, a distributed and dynamic improvement solution based on the Deep Deterministic Policy Gradient (DDPG) approach. Harmony intelligently determines each user’s transmission rate by combining feedback from user applications and MEC server metrics, ensuring superior QoE levels, energy efficiency, and fairness. The trained Harmony can be adapted to various Adaptive BitRate (ABR) algorithms, providing scalability and immediate applicability in existing LEO networks. It can also achieve improved performance in dynamic user environments. Simulation results demonstrate that Harmony improves energy efficiency and fairness, while maintaining high QoE levels and reducing MEC traffic overhead by 28.1% to 62.6%.

Viewport in immersive media corresponds to the field of view (FoV), playing a critical role in both data transmission volume and user experience. However, instantaneous and highly dynamic interactions often conflict with segment-based transmission modes, resulting in substantial redundant data transmission and wastage of valuable resources. In this paper, we analyze data from an open-source dataset and our self-collected records to investigate the interactive characteristics of viewers in immersive scenes, including focus time, viewing area scope, movement direction, and tile access probability. Based on empirical statistical inference, we innovatively introduce the concept of an irregular, expandable, and directional extended field of view (EoV) to describe the dynamically variable area mimicking human visual motion. Furthermore, we propose a motion-aware tile-based adaptive control scheme for viewing areas, named VAAC-IM, designed to enable flexible transmission of immersive media. Specifically, we developed an FoV prediction model based on ConvLSTM, leveraging spatiotemporal features from historical viewing records to provide advanced predictions of visual motion preferences. Subsequently, we model the viewing area control process as a constrained submodular minimization problem, dynamically managing irregular EoV area using marginal effects. Finally, we perform a comprehensive validation. The results demonstrate that VAAC-IM significantly enhances performance in terms of reducing black edge coverage, minimizing data volume, lowering latency, and improving overall user experience.

Recently, the popularity of crowdsourced live streaming (CLS) has increased significantly. To overcome the bandwidth limitations of the broadcaster’s stream in the first mile, some research has introduced video super-resolution (VSR) algorithms at the source server. However, this implementation brings an additional computational burden. In VSR-enabled CLS, the stream push bitrate of the broadcaster will directly determine the volume of data transmitted in the first mile and processed by the source server. An inappropriate upload bitrate can lead to unacceptable delays and resource wastage. To address this issue, this paper proposes a VSR-UAiC framework, which implements an upload adaptive bitrate (ABR) control in VSR-enabled CLS. The VSR-UAiC framework is capable of perceiving multi-dimensional information, including network conditions, computing power, video content, and viewer requests. VSR-UAiC employs an end-to-end reinforcement learning (RL) algorithm to train agents for obtaining the optimal upload ABR strategy. A series of experiments have demonstrated the superiority of the VSR-UAiC framework over alternative solutions in terms of latency, cost, and system capacity.

Non-terrestrial networks are regarded as crucial infrastructure in forthcoming 6G networks. Edge caching services can be deployed on satellites to optimize the transmission delay of non-terrestrial networks. The dynamically changing user requests and the limited cache space challenge the decision-making process for satellite caching. This paper proposes a Handover-Aware Cache Replacement (HACR) strategy to dynamically replace cache content with satellite mobility. The strategy incorporates the deterministic mobility of satellites to make optimal caching decisions over time and utilizes deep reinforcement learning to implement the strategy. We develop a simulation platform based on the configuration of the Starlink constellation and compare our proposed strategy with conventional methods. The results demonstrate that HACR achieves a 16.74% improvement in cache hit rate and a 48.9 % improvement in stability of cache hit rate compared to the state-of-the-art cache strategy.

The viewport in immersive media, exemplified by panoramic video, corresponds to the field of view (FoV) and significantly impacts both the volume of data transmission and the user experience. However, the instantaneous and highly dynamic nature of user interactions poses a challenge to the traditional segment-based transmission mode, creating a conflict between the need for real-time responsiveness and the structured nature of data delivery. It results in the transmission of substantial redundant data, leading to wastage of valuable resources. In this paper, we analyze data from open-source dataset and our self-collected records to investigate the interactive characteristics of viewer in immersive scenes, e.g., focus time, viewing area scope, and movement direction. Based on statistical inferences, we introduce the concept of extended field of view (EoV) to describe irregular viewing areas that mimic the moving features of the human visual field. A motion-aware tile-based viewing area adaptive control scheme, termed VAAC-IM, is designed for transmitting immersive media flexibly. We model viewing area control process as a constrained submodular minimization problem, and design a greedy search-based strategy to dynamically control irregular EoV area. Finally, we perform a comprehensive validation. The results demonstrate that VAAC-IM significantly enhances performance in terms of reducing black edge coverage, minimizing data volume, lowering latency, and improving overall user experience.