StarStream: Live Video Analytics over Space Networking

TL;DR

StarStream is a novel framework designed to enable real-time live video analytics over Low Earth Orbit satellite networks, addressing uplink bottlenecks and network volatility with adaptive streaming techniques.

Contribution

The paper introduces StarStream, a new LSN-adaptive streaming framework for live video analytics, featuring a Transformer-based performance predictor and content-aware optimizer.

Findings

StarStream improves LVA performance over LEO satellite networks.

Uplink bottleneck significantly impacts service quality.

Adaptive streaming enhances robustness under volatile network conditions.

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.