S. Zehtabian, M. Razghandi, L. Bölöni, and D. Turgut. Predictive Caching for AR/VR Experiences in a Household Scenario. In IEEE ICNC'20, pp. 591–595, February 2020.
Augmented/virtual reality (AR/VR) technologies can be deployed in a household environment for applications such as checking the weather or traffic reports, watching a summary of news, or attending classes. Since AR/VR applications are highly delay sensitive, delivering these types of reports in maximum quality could be very challenging. In this paper, we consider that users go through a series of AR/VR experience units that can be delivered at different experience quality levels. In order to maximize the quality of the experience while minimizing the cost of delivering it, we aim to predict the users’ behavior in the home and the experiences they are interested in at specific moments in time. We describe a deep learning based technique to predict the users’ requests from AR/VR devices and optimize the local caching of experience units. We evaluate the performance of the proposed technique on two real-world datasets and compare our results with other baselines. Our results show that predicting users’ requests can improve the quality of experience and decrease the cost of delivery.
@inproceedings{Zehtabian-2020-ICNC,
author = "S. Zehtabian and M. Razghandi and L. B{\"o}l{\"o}ni and D. Turgut",
title = "Predictive Caching for AR/VR Experiences in a Household Scenario",
booktitle = "IEEE ICNC'20",
pages = "591-595",
month = "February",
year = "2020",
abstract = {Augmented/virtual reality (AR/VR) technologies can be deployed in a household environment for applications such as checking the weather or traffic reports, watching a summary of news, or attending classes. Since AR/VR applications are highly delay sensitive, delivering these types of reports in maximum quality could be very challenging. In this paper, we consider that users go through a series of AR/VR experience units that can be delivered at different experience quality levels. In order to maximize the quality of the experience while minimizing the cost of delivering it, we aim to predict the users’ behavior in the home and the experiences they are interested in at specific moments in time. We describe a deep learning based technique to predict the users’ requests from AR/VR devices and optimize the local caching of experience units. We evaluate the performance of the proposed technique on two real-world datasets and compare our results with other baselines. Our results show that predicting users’ requests can improve the quality of experience and decrease the cost of delivery. },
}
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