Joint video summarization and transmission adaptation for energy-efficient wireless video streaming

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Performance analysis of H.264 encoder for high-definition video transmission over ultra-wideband communication link.

With the technological advancement, entertainment has become revolutionized and the High-definition (HD) video has become a common feature of our modern amusement devices. Moreover, the demand for wireless transmission of HD video is rising increasingly for its ubiquitous nature, easy installation and relocation. The high bandwidth requirement is the main concern for wireless transmission of high quality video streams. Research has been going on by the consumer electronics industry to provide different solutions of this issue, for the last few years. In this research work, HD video transmission feasibility using the Ultra-wideband (UWB) communication channel is analyzed. The UWB channel is selected for its short-range, high-speed data transmission capability at low-cost, and low-power consumption. The maximum transmitting range of this technology is about 10 m at 100 Mbps data rate. Simulation is conducted by controlling key parameters, such as, in-loop deblocking filter, group of pictures, and quantization parameter of an H.264/AVC encoder. Here, standard HD video streams with different motion characteristics are used, and the impact of these parameters change on the reconstructed video quality and the broadcasting data rate are analyzed. Finally, a generalized parameters settings, and a video content dependent settings for an H.264/AVC encoder are proposed for different bandwidth requirements, as well as acceptable video quality. Performance evaluation of these parameters settings is performed, and the results are quite satisfactory as long as the symbol energy to noise power density ratio, Es/No, is above 15. With the proposed parameters settings, maximum 20 Mbps data rate is achieved with 33.5 dB Y-PSNR

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

The Effective Transmission and Processing of Mobile Multimedia

Ph.DDOCTOR OF PHILOSOPH

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen