StarStream: Live Video Analytics over Space Networking

Miao Zhang, Jiaxing Li, Haoyuan Zhao, Linfeng Shen, Jiangchuan Liu

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, are 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.

Primary Subject Area: [Systems] Transport and Delivery

Secondary Subject Area: [Systems] Systems and Middleware

Relevance To Conference: Our work lies at the intersection of networking and multimedia systems, with a particular focus on streaming videos over the newly emerged low Earth orbit satellite networks (LSN) for online analytics. LSNs provide high-speed, cost-effective global internet access, extending connectivity to previously unreachable, unserved, or underserved populations. This capability is crucial for bridging the digital divide. Our work introduces a pioneering LSN-aware video streaming framework for online analytics, which is a critical advancement in multimedia processing. Through comprehensive measurements and evaluations, this work demonstrates the practical integration of multimedia analytics systems on LSN, offering valuable insights into the development of general multimedia systems on emerging network infrastructures.

Submission Number: 1908