Live streaming of immersive multimedia content, e.g., 360-degree videos, is getting popular due to the recent availability of commercial devices that support interacting with such content such as smartphones/tablets and head-mounted displays. Streaming live content to mobile users using individual connections (i.e., unicast) consumes substantial network resources and does not scale to large number of users. Multicast, on the other hand, offers a scalable solution but it introduces multiple challenges, including handling user interactivity, ensuring smooth quality, conserving the energy of mobile receivers, and achieving fairness among users. We propose a new solution for the problem of live multicast streaming of 360-degree videos to mobile users, which addresses the aforementioned challenges. The proposed solution, referred to as VRCast, is designed for cellular networks that support multicast, such as LTE. We show through trace-driven simulations that VRCast outperforms the closest algorithms in the literature by wide margins across several performance metrics. For example, compared to the state-of-the-art, VRCast improves the viewport quality by up to 2.5 dB. We have implemented VRCast in an LTE testbed to show its practicality. Our experimental results show that VRCast ensures smooth video quality and saves energy for mobile devices.

EEG-based authentication is an emerging research field. In this work, a realistic authentication system using Electroencephalography signals, was developed aiming to show that brain signals contain sufficient information to be used in security systems. The dataset used was composed of 29 users on 4 different days via the cheap Neurosky Mindwave headset with a single dry electrode, and 10 users on 3 different days via Emotiv with 14 electrodes. Various techniques, features, and algorithms were examined to achieve the highest security. Experiments indicated that the system proposed can scale with respect to increasing number of users in the datasets. The system successfully handles users authenticating from multiple days not used in training the model with high accuracy. A false acceptance error (FA) of 3% was achieved, with a higher false rejection error (FR) of 48%, yielding an overall accuracy (ACC) of around 80% using the Mindwave dataset, and a FA of 0.3%, a FR of 13.93% resulting in an ACC of 92.88% using Emotiv. These results are promising for an authentication system, because the system is conservative, only allowing correct users to enter-even at the expense of multiple attempts-while successfully refusing to grant access to impersonating users.