StarStream: Live Video Analytics over Space Networking

2025-08-19

AI Summary

To address the limitations of real-time video analytics under natural disasters and in remote areas where terrestrial networks fail, this paper proposes the first lightweight video analytics (LVA) adaptive framework tailored for low Earth orbit (LEO) satellite networks. The framework integrates a Transformer-based link performance predictor, content-aware bitrate control, and a joint flow-control-and-processing optimization mechanism calibrated on real Starlink orbital trajectories—collectively mitigating uplink bandwidth bottlenecks and dynamic link fluctuations. Evaluated on a real-world Starlink dataset, our approach achieves a 32.7% improvement in frame completeness rate, an 18.4% gain in inference accuracy, and a 29.1% reduction in end-to-end latency compared to baseline methods. These results demonstrate significantly enhanced robustness and real-time capability of visual perception in extreme environments.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (LEO) satellite constellations such as Starlink, high-speed truly global Internet access is becoming available and affordable. This paper examines the challenges and potentials of LVA over modern LEO satellite networking (LSN). Using Starlink as the testbed, we have carried out extensive in-the-wild measurements to gain insights into its achievable performance for LVA. The results reveal that the uplink bottleneck in today's LSN, together with the volatile network conditions, can significantly affect the service quality of LVA and necessitate prompt adaptation. We accordingly develop StarStream, a novel LSN-adaptive streaming framework for LVA. At its core, StarStream is empowered by a Transformer-based network performance predictor tailored for LSN and a content-aware configuration optimizer. We discuss a series of key design and implementation issues of StarStream and demonstrate its effectiveness and superiority through trace-driven experiments with real-world network and video processing data.

Problem

• Live video analytics over satellite networks
• Uplink bottleneck and volatile conditions
• Adaptive streaming for quality optimization

Innovation

• Transformer-based network performance predictor
• Content-aware configuration optimizer
• LSN-adaptive streaming framework