7 research outputs found
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research
A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions
The Internet has made several giant leaps over the years, from a fixed to a
mobile Internet, then to the Internet of Things, and now to a Tactile Internet.
The Tactile Internet goes far beyond data, audio and video delivery over fixed
and mobile networks, and even beyond allowing communication and collaboration
among things. It is expected to enable haptic communication and allow skill set
delivery over networks. Some examples of potential applications are
tele-surgery, vehicle fleets, augmented reality and industrial process
automation. Several papers already cover many of the Tactile Internet-related
concepts and technologies, such as haptic codecs, applications, and supporting
technologies. However, none of them offers a comprehensive survey of the
Tactile Internet, including its architectures and algorithms. Furthermore, none
of them provides a systematic and critical review of the existing solutions. To
address these lacunae, we provide a comprehensive survey of the architectures
and algorithms proposed to date for the Tactile Internet. In addition, we
critically review them using a well-defined set of requirements and discuss
some of the lessons learned as well as the most promising research directions
Design and analysis of network coding schemes for efficient fronthaul offloading of fog-radio access networks
In the era of the Internet of Things (IoT), everything will be connected. Smart homes and cities, connected cars, smart agriculture, wearable technologies, smart healthcare, smart sport, and fitness are all becoming a reality. However, the current cloud architecture cannot manage the tremendous number of connected devices and skyrocketing data traffic while providing the speeds promised by 5G and beyond. Centralised cloud data centres are physically too far from where the data originate (edge of the network), inevitably leading to data transmission speeds that are too slow for delay-sensitive applications. Thus, researchers have proposed fog architecture as a solution to the ever-increasing number of connected devices and data traffic.
The main idea of fog architecture is to bring content physically closer to end users, thus reducing data transmission times. This thesis considers a type of fog architecture in which smart end devices have storage and processing capabilities and can communicate and collaborate with each other. The major goal of this thesis is to develop methods of efficiently governing communication and collaboration between smart end devices so that their requests to upper network layers are minimised. This is achieved by incorporating principles from graph theory, network coding and machine learning to model the problem and design efficient network-coded scheduling algorithms to further enhance achieved performance. By maximising end users' self-sufficiency, the load on the system is decreased and its capacity increased. This will allow the central processing unit to manage more devices which is vital, given that more than 29 billion devices will connect to the infrastructure by 2023 \cite{Cisco1823}.
Specifically, given that the limitations of the smart end devices and the system in general lead to various communication conflicts, a novel network coding graph is developed that takes into account all possible conflicts and enables the search for an efficient feasible solution. The thesis designs heuristic algorithms that search for the solution over the novel network coding graph, investigates the complexity of the proposed algorithms, and shows the offloading strategy's asymptotic optimality.
Although the main aim of this work is to decrease the involvement of upper fog layers in serving smart end devices, it also takes into account how much energy end devices would use during collaborations. Unfortunately, a higher system capacity comes at the price of more energy spent by smart end devices; thus, service providers' interests and end users' interests are conflicting. Finally, this thesis investigates how multihop communication between end devices influences the offloading of upper fog layers. Smart end devices are equipped with machine learning capabilities that allow them to find efficient paths to their peers, further improving offloading.
In conclusion, the work in this thesis shows that by smartly designing and scheduling communication between end devices, it is possible to significantly reduce the load on the system, increase its capacity and achieve fast transmissions between end devices, allowing them to run latency-critical applications