Public defence in Networking Technology, M.Sc. Oussama El Marai

This dissertation explores how video streaming latency can be improved to accommodate different smart city applications.
Public defence from the Aalto University School of Electrical Engineering, Department of Information and Communications Engineering
Doctoral hat floating above a speaker's podium with a microphone

The title of the thesis: Improving Live Video Streaming Performance for Smart City Services

Doctoral student: Oussama El Marai
Opponent: Prof. Sasu Tarkoma, University of Helsinki
Custos: Prof. Jukka Manner, Aalto University School of Electrical Engineering, Department of Information and Communications Engineering 

Our world is rapidly moving in all its aspects toward a more digitized and connected life, including transportation, education, farming, and healthcare. A major enabler for such transformation is ICT-related tremendous innovations in networking, computation, and storage, both in software and hardware at affordable prices. Owing to these phenomenal advances, many revolutionary paradigms, such as multi-access edge computing, self-driving vehicles, and Smart Cities (SC), have emerged, promising rosy prospects and a flourishing future. Most of today's applications and systems (e.g., over-the-top and surveillance platforms) require video streaming as a key technology. Video streaming applications rank as the most bandwidth-intensive services, especially when delivered at higher resolutions. Fortunately, 5G technology is already available and promises high bandwidth and low latency. In addition, it requires huge data storage spaces when historical data is needed, which no longer becomes an issue with the dawn of edge and cloud computing. The target consumer (i.e., humans or machines) might demand heavy computation resources, often requiring GPU, which is also nowadays readily available and affordable. 

This dissertation is all about harnessing video streaming technology for enabling SC services and paradigms, such as self-driving vehicles. Towards this end, we start by addressing the problem of improving video streaming performance in terms of delivered video quality, stall-free sessions, and low latency streaming, for various services, including OTT services and some use cases of self-driving vehicles. As data is the fuel that empowers most SC systems and services, we propose a cost-efficient and sustainable solution to create the digital twin of city roads, which mainly relies on video streaming data. The proposed solution represents an essential step towards realizing the SC paradigm and would create a valuable data asset that feeds and benefits various systems and domains such as intelligent transportation systems, national security, etc. Owing to the extreme importance of situational awareness in SC, notably in dense urban areas, we leverage the proposed digital twinning solution and machine learning techniques to raise the awareness of connected vehicles about their surroundings, as well as overall street awareness per defined regions while accounting for the amount of transmitted data over the network to avoid video streaming performance degradation.

Keywords: video streaming; smart city; digital twin; intelligent transportation systems

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