Improving Live Video Streaming Performance for Smart City Services

Thumbnail Image
Files
isbn9789526418025.pdf (13.75 MB)
Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2024-05-22
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Author
Date
2024
Department
Informaatio- ja tietoliikennetekniikan laitos
Department of Information and Communications Engineering
Major/Subject
Mcode
Degree programme
Language
en
Pages
131 + app. 89
Series
Aalto University publication series DOCTORAL THESES, 95/2024
Abstract
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, have emerged, promising rosy prospects and a flourishing future. An eminent feature of these futuristic technologies is automation, where objects can communicate (i.e., sending and receiving data), understand their environment, and adapt to changing conditions by taking the right decisions. Also, stringent requirements (e.g., low latency communication) might be needed by many services for their proper functioning. To successfully accomplish these tasks, many paradigms (e.g., software-defined networking and machine learning techniques) should be involved at different levels (e.g., network and decision-making levels). 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, such as FHD and 4K. Fortunately, 5G technology is already available and promises higher bandwidth that can reach up to 20GB. 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 processing, which is also nowadays readily available and affordable. This dissertation is all about harnessing video streaming technology for enabling Smart City 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 video streaming services and some use cases of self-driving vehicles. As data is the fuel that empowers most Smart City 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 Smart City paradigm and would create a valuable data asset that feeds and benefits various systems and domains such as intelligent transportation systems and tourism. Owing to the extreme importance of situational awareness in Smart Cities, 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.
Description
Supervising professor
Manner, Jukka, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Keywords
video streaming technology, vehicles, Smart City, transportation
Other note
Parts
  • [Publication 1]: O. El Marai, J. Prados-Garzon, M. Bagaa and T. Taleb. Ensuring High QoE for DASH-Based Clients Using Deterministic Network Calculus in SDN Networks. In GLOBECOM 2019 - IEEE Global Communications Conference, pp. 1-6, 2019.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202306053559
    DOI: 10.1109/GLOBECOM38437.2019.9013633 View at publisher
  • [Publication 2]: O. El Marai, M. Bagaa and T. Taleb. Coalition Game-based Approach for Improving the QoE of DASH-based Streaming in Multi-servers Scheme. In GLOBECOM 2020 - IEEE Global Communications Conference, pp. 1-6, 2020.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202102262103
    DOI: 10.1109/GLOBECOM42002.2020.9322450 View at publisher
  • [Publication 3]: O. El Marai and T. Taleb. Smooth and Low Latency Video Streaming for Autonomous Cars During Handover. in IEEE Network, 34, 6, 302-309, November/December 2020.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202101251443
    DOI: 10.1109/MNET.011.2000258 View at publisher
  • [Publication 4]: G. Pastor, E. Mutafungwa, J. Costa-Requena, X. Li, O. El Marai, N. Saba, A. Zhanabatyrova, Y. Xiao, T. Mustonen, M. Myrsky, L. Lammi, U. Zakir Abdul Hamid, M. Boavida, S. Catalano, H. Park, P. Vikberg, S. Pukkila, V. Lyytikainen. Qualifying 5G SA for L4 Automated Vehicles in a Multi-PLMN Experimental Testbed. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), pp. 1-3, 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202109028833
    DOI: 10.1109/VTC2021-Spring51267.2021.9448788 View at publisher
  • [Publication 5]: E. Mutafungwa, G. Pastor, J. Costa-Requena, X. Li, O. El Marai, N. Saba, A. Zhanabatyrova, Y. Xiao, T. Mustonen, M. Myrsky, L. Lammi, U. Zakir Abdul Hamid, M. Boavida, S. Catalano, H. Park, P. Vikberg, S. Pukkila, V. Lyytikainen. Field demonstration of service continuity for remote driving in a 5G multi-PLMN environment. 2021 IEEE 5G World Forum (WF-5G), pp. 1-3, 2021.
  • [Publication 6]: O. El Marai, T. Taleb and J. Song. Roads Infrastructure Digital Twin: A Step Toward Smarter Cities Realization, in IEEE Network, vol. 35, no. 2, pp. 136-143, March/April 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202105266999
    DOI: 10.1109/MNET.011.2000398 View at publisher
  • [Publication 7]: O. El Marai, T. Taleb and J. Song. AR-based Remote Command and Control Service: Self-driving Vehicles Use Case. in IEEE Network, 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202212146944
    DOI: 10.1109/MNET.119.2200058 View at publisher
  • [Publication 8]: H. Masuda, O. El Marai, M. Tsukada, T. Taleb and H. Esaki. Featurebased Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks. in IEEE Transactions on Vehicular Technology, 2022.
    DOI: 10.1109/TVT.2022.3211852 View at publisher
  • [Publication 9]: O. El Marai, S. Messinis, N. Doulamis, T. Taleb, J. Manner. A Multiview Clustering Approach for Raising the Situational Awareness of the Roads using 360° Video Streaming. Journal Paper, Status: Under review, Submitted on 29-Oct-2023.
Citation
Permanent link to this item
https://urn.fi/URN:ISBN:978-952-64-1802-5
Collections
[diss] Sähkötekniikan korkeakoulu / ELEC