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|Title:||Optimizing networked drones and video delivery in wireless network|
|Contributors:||Electrical Engineering Department|
|Publisher:||Princeton, NJ : Princeton University|
|Abstract:||UAVs (unmanned aerial vehicles) equipped with high-end cameras have become increasingly popular among consumers. A network of drones can be deployed for scene monitoring and entertainment, such as disaster response, virtual reality and formation of drones presenting light show. The recent trend of using commercial drones to capture live video of extreme action sports suggest their potential usefulness to capture high-action sports played on large fields, where conventional systems today require 30-50 expensive stationary cameras and tedious labor work to steer them. However, managing a network of drones in real-time capturing and large scene reconstruction is challenging, due to the requirement of fast response and accurate reconstruction. Distributed approaches yield suboptimal solutions from lack of coordination, which can result in redundant data collection and processing, while centralized approaches are limited by the round-trip time between drones and the controller, which can be a critical issue when the drones are far from the controller. In this thesis, I propose a fog-networking based system architecture to automatically coordinate a network of drones equipped with cameras to capture and broadcast the dynamically changing scenes of interest in a sports game, and a collaborative capturing and processing system for large scene reconstruction, aiming to achieve good reconstruction accuracy, fast response and efficient processing. Consumer UAVs today use fixed-bitrate video streaming where users configure the resolution (4K or 1080p). However, applications with real-time streaming that deploy UAVs in the wild will require adaptive video streaming to tackle uncertain wireless link capacities and meet their video quality requirements. My work on adaptive video streaming in Whitespace channel proposes a robust MDP-based solution that proactively adapts to fast-varying channel conditions, and later I explore how adaptive video streaming can be adapted to the networked drones streaming system to provide significant gains for UAV streaming.|
|Alternate format:||The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu|
|Type of Material:||Academic dissertations (Ph.D.)|
|Appears in Collections:||Electrical Engineering|
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