Cloud VR/AR/MR (Virtual Reality, Augmented Reality, and Mixed Reality) services representa high-level architecture that combines large scale computer resources in a data-center structurestyle set up to render VR/AR/MR services using a combination of very high bandwidth, ultralow latency, high throughput, latest 5G (5th Generation) mobile networks to the end users.  VR refers to a three-dimensional computer-generated virtual environment made up ofcomputers, which can be explored by people for real time interaction. AR amplifies humanperception of the real world through overlapping of computer-generated graphics or interactivedata on a real-world image for enhanced experience.  According to the Virtual Reality Society’s account of the history of VR, it started from the360-degree murals from the nineteenth century [18]. Historically, live application of AR wasdisplayed when Myron Kruger used a combination of video cameras and projector in aninteractive environment in 1974. In 1998, AR was put into live display with the casting of avirtual yellow line marker during an NFL game. However, personal, and commercial use ofVR/AR was made possible starting with release of a DIY (Do it Yourself) headset calledGoogle Cardboard in 2014 by Google, which made use of a smartphone for the VR experience.In 2014, Samsung also introduced Gear VR which officially started the competition for VRdevices. Subsequently In 2014, Facebook acquired Oculus VR with the major aim ofdominating the high-end spectrum of VR headset [18]. Furthermore, wider adoption of ARbecame enhanced with the introduction of Apple’s ARKit (Augmented Reality Kit) whichserves as a development framework for AR applications for iPhones and iPads [18].  The first application of VR devices in the health industry was made possible due to healthworkers’ need to visualize complex medical data during surgery and planning of surgery in1994. Since then, commercial production of VR devices and availability of advanced networkand faster broadband have increased the adoption of VR services in the healthcare industryespecially in planning of surgery and during surgery itself [16]. Overall, the wide availabilityof VR/AR terminals, displays, controllers, development kits, advanced network, and robustbandwidth have contributed to making VR and AR services to be of valuable and importanttechnologies in the area of digital entertainment, information, games, health, military and soon. However, the solutions or services needed for the technology required an advancedprocessing platform which in most cases is not cost efficient in single-use scenarios.  The kind of devices, hardware, software required for the processing and presentation ofimmersive experiences is often expensive and dedicated to the current application itself.Technological improvement in realism and immersion means increase in cost of ownershipwhich often affected cost-benefit consideration, leading to slower adoption of the VR services[14] [15]. This is what has led to development of cloud VR services, a form of data-centerbased system, which serves as a means of providing VR services to end users from the cloudanywhere in the world, using its fast and stable transport networks. The content of the VR isstored in the cloud, after which the output in form of audio-visuals is coded and compressedusing suitable encoding technology, and thereafter transmitted to the terminals. The industrywide acceptance of the cloud VR services, and technology has made available access to payper-use-basis and hence access to high processing capability offered, which is used in iipresenting a more immersive, imaginative, and interactive experience to end users [11] [12].However, cloud VR services has a major challenge in form of network latency introduced fromcloud rendering down to the display terminal itself. This is most often caused by otherperformance indicators such as network bandwidth, coding technology, RTT (Return TripTime) and so on [19]. This is the major problem which this thesis is set to find out.  The research methodology used was a combination of empirical and experimental method,using quantitative approach as it entails the generation of data in quantitative form availablefor quantitative analysis. The research questions are: Research Question 1 (RQ1): What are the latency related performance indicators ofnetworked immersive media in mobile health applications? Research Question 2 (RQ2): What are the suitable network structures to achieve an efficientlow latency VR health application?  The answers gotten from the result analysis at the end of the simulation, show thatbandwidth, frame rate, and resolution are very crucial performance indicator to achieve theoptimal latency required for hitch-free cloud VR user experience, while the importance of otherindicators such as resolution and coding standard cannot be overemphasized. Combination ofedge and cloud architecture also proved to more efficient and effective for the achievement ofa low-latency cloud VR application functionality.  Conclusively, the answer to research question one was that, the latency relatedperformance indicators of networked immersive media in mobile health applications arebandwidth, frame rate, resolution, coding technology. For research question two, suitablenetwork structures includes edge network, cloud network and combination of cloud and edgenetwork, but in order to achieve an optimally low-latency network for cloud VR mobile healthapplication in education, combination of edge and cloud network architecture is recommended

abstract: With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices. This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications. / Dissertation/Thesis / Masters Thesis Computer Science 2015