CMU-CS-24-128
Computer Science Department
School of Computer Science, Carnegie Mellon University



CMU-CS-24-128

Benchmarking Drone Video Stream Latency In SteelEagle

Aditya Chanana, Mihir Bala, Thomas Eiszler, James Blakley,
Mahadev Satyanarayanan

May 2024

CMU-CS-24-128.pdf


Keywords: Edge computing, drones, mobile networks, latency, bandwidth, edge-native applications, computer vision, machine learning

Low latency is critical for edge-enabled autonomous drones to do real-time computer vision tasks like target tracking effectively. We discuss our work on analyzing the latency of the real-time drone video stream using the SteelEagle autonomous drone system. By varying factors such as the cloudlet location, network type, and the video decoding library used, we show that the latency is bottlenecked not by the network but by the decoding of the RTSP video stream generated by the drone.

11 pages


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