Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific lightweight , no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric. INDEX TERMS Quality assessment, no reference, gaming video streaming, machine learning, regression, quality of experience, video quality metrics.
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.
Integration of miniature sensors composes a wireless body area network (WBAN), which enables remote health monitoring. To make this technology widely acceptable in the society, some studies suggest commonly used gadgets such as cell phones or laptops as a hub for WBANs. In these cases, envisaged medical and non-medical applications of WBANs must have the same priority unless in emergency situations. Also, medical applications of WBANs need some strict requirements that are not that important for non-medical applications, such as very low-power consumption or reliability. In addition, channel condition may change in WBANs because of fading effects and this causes packet loss. Therefore proper traffic prioritisation, high reliability and efficient channel utilisation are vitally important issues in these networks. In this study, the authors improve the performance of the medium access control (MAC) protocol of WBANs using an adaptive resource allocation and traffic prioritisation according to the medical situation of user and channel condition. Through adaptively separating and managing the possible traffics of WBANs, the heterogeneous requirements of different applications are provided. Analytical and simulation results show that the proposed MAC protocol outperforms IEEE 802.15.4 and IEEE 802.15.6 MAC protocols in terms of power consumption as well as the channel utilisation and reliability.
Spectrum scarcity and dramatically increasing demand for high data rate and high quality video live streaming are of future cellular network design challenges. As a solution to this problem, cache-enabled cellular network architecture has been recently proposed. Device-to-Device (D2D) communications can be exploited for distributed video content delivery, and devices can be used for caching of the video files. This can increase the capacity and reduce the end-to-end delay in cellular networks. In this paper, we propose a new scheme for video distribution over cellular networks by exploiting full-duplex (FD) radios for D2D devices in two scenarios; i) two nodes exchange their desired video files simultaneously, and ii) each node can concurrently transmit to and receive from two different nodes. In the latter case, an intermediate transceiver can serve one or multiple users' file request(s) whilst capturing its desired file from another device in the vicinity. Mathematical expressions along with extensive simulations are used to compare our proposed scheme with a half-duplex (HD) scheme to show the achievable gains in terms of sum throughput, active links, and delay. We will also look into the energy cost for achieving the improvements provided by operation in FD mode.
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