@inproceedings {42695,
	title = {Video Analytics in Elite Soccer: A Distributed Computing Perspective},
	journal = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
	year = {2022},
	month = {06/2022},
	pages = {221-225},
	publisher = {IEEE},
	address = {Trondheim, Norway},
	abstract = {Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA{\textquoteright}s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.\&nbsp;},
	keywords = {analytics, football, soccer, Video},
	doi = {10.1109/SAM53842.2022.9827827},
	url = {https://ieeexplore.ieee.org/document/9827827},
	author = {Jha, Debesh and Rauniyar, Ashish and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l and Bagci, Ulas}
}
@inproceedings {23463,
	title = {Analysing User Satisfaction in Next Generation Networks for Multimedia Multicast Transmission},
	journal = {The 7th International Workshop on Quality of Multimedia Experience (QoMEX 2015)},
	year = {2015},
	publisher = {IEEE},
	abstract = {Video users in a multicast group may be highly heterogeneousin terms of individual channel conditions and requirements forvideo transmission, making it a challenging task for the network to optimally configure the resource management.\&nbsp;In this paper, we consider a mathematical model for the choice of the optimum number of transmission opportunities, based on a Markov chain representation of the wireless channel, where each channel state is associated to a different quality corresponding to a choice of video layer and Modulation and Coding Scheme (MCS).In case of multicast transmissions, the selection of the video layer is based on the aggregate channel conditions of all users and a collection of coordination rules.\&nbsp;We evaluate how the cross-layer optimisation performs and also define a taxonomy of user quality perception. We further investigate how users{\textquoteright} satisfaction levels vary in the multicast session.Performance evaluation results show that cross-layer optimisation significantly outperforms sequential and independent scheme for multicast video transmissions, conforming the strong need for cross-layer solutions in video transmission, however, this improvement is heavily related to the resource allocation policy of the operator.},
	keywords = {channel rate, multicast, user satisfaction, Video},
	author = {Ahmed, Iffat and Petlund, Andreas}
}
