Quality-of-Experience Provisioning for Ubiquitous Wireless Imaging Applications

Recent advances in imaging hardware and wireless communications have fostered the deployment of embedded camera sensors in various wireless imaging applications, such as surveillance, intelligent transportation, remote healthcare, and consumer electronics and entertainment. The increasing demand of bandwidth from these applications puts pressure on the wireless networking infrastructure and the battery-powered camera sensors.

We are investigating new communication solutions for wireless imaging applications. Our research goal is to provide satisfactory quality of experience (QoE) to users of wireless imaging applications using the minimum energy and bandwidth resources in wireless camera networks. We are investigating mechanisms that can efficiently predict the perceptual quality of networked videos as evaluated by human users. Based on the properties of perceptual quality, we are designing new communication protocols to effectively control QoE in dynamic network environments.

Our research will bridge the gap between the studies in perceptual video quality and the design of communication protocols. Involving ideas from wireless networking, video processing, machine learning, and human factors, our research will inspire new interdisciplinary research in quality provisioning for many other wireless sensing applications.




 

Related Publications

1. M. N. Sadat, E. V. Alfonso, R. Dai, Z. Huang, Y. Fu, S. Lin, “QoE-Driven Cross-Layer Design for Video Communication over Software-Defined Radio”, IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, Jan. 2021.
2. M. N. Sadat, E. V. Alfonso, R. Dai, Z. Huang, Y. Fu, S. Lin, “QoE-VS: A Cross-Layer QoE-Aware Video Streaming Platform Using Software-Defined Radio”, IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, Canada, 2020.
3. L. Kong and R. Dai, "Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance Systems", ACM Transactions on Multimedia Computing Communications and Applications, Vol. 14, No. 3, Article 72, July 2018.
4. L. Kong, J. Zhu, R. Dai, and M. N. Sadat, "Impact of Distributed Caching on Video Streaming Quality in Information Centric Networks", IEEE International Symposium on Multimedia (ISM), Dec. 2017.
5. A. Hameed, B. Balas, and R. Dai, "Thin-Slice Vision: Inference of Confidence Measure from Perceptual Video Quality", SPIE Journal of Electronic Imaging, vol. 25, no. 6, Dec. 2016.
6. A. Hameed, R. Dai, and B. Balas, "A Perceptual Video Quality Prediction Model and its Application in Forward Error Correction for Wireless Multimedia Communications", IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 764-774, Apr. 2016.
7. A. Hameed, R. Dai, and B. Balas, "Predicting the Perceptual Quality of Networked Video through Light-Weight Bitstream Analysis", IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), May 2014.