Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Neural Video Recovery for Cloud Gaming

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design

Adaptive 3D web-based environment for heterogeneous volume objects.

The Internet was growing fast on the last decade. Interaction and visualisation became an essential feature online. The demand for online modelling and rendering in a real-time, adaptive and interactive manner exceeded the growth and development of the hardware resources including computational power and memories. Building up and accessing an instant 3D Web-based and plugin-free platform started to be a must in order to generate 3D volumes. Modelling and rendering complicated heterogeneous volumes using online applications requires good Internet bandwidth and high computational power. A large number of 3D modelling tools designed to create complicated models in an interactive manner are now available online, the problem of using such tools is that the user needs to acquire a certain level of modelling knowledge In this work, we identify the problem, introduce the theoretical background and discuss the theory about Web-based modelling and rendering, including client- server approach, scenario optimization by solving constraint satisfaction problem, and complexity analysis. We address the challenges of designing, implementing and testing an online, Web-based, instant 3D modelling and rendering environment and we discuss some of its characteristics including adaptivity, platform independence, interactivity, and easy-to-use after presenting the theoretical part of implementing such an environment. We also introduce platform-independent modelling and rendering environment for complicated heterogeneous volumes with colour attributes based on client- server architecture. The work includes analysis and implementation for different rendering approaches suitable for different kind of users. We also discuss the performance of the proposed environment by comparing the rendering approaches. As an additional feature of our modelling system, we discuss aspects of securing the model transferring between client and the server

Network reputation-based quality optimization of video delivery in heterogeneous wireless environments

The mass-market adoption of high-end mobile devices and increasing amount of video traffic has led the mobile operators to adopt various solutions to help them cope with the explosion of mobile broadband data traffic, while ensuring high Quality of Service (QoS) levels to their services. Deploying small-cell base stations within the existing macro-cellular networks and offloading traffic from the large macro-cells to the small cells is seen as a promising solution to increase capacity and improve network performance at low cost. Parallel use of diverse technologies is also employed. The result is a heterogeneous network environment (HetNets), part of the next generation network deployments. In this context, this thesis makes a step forward towards the “Always Best Experience” paradigm, which considers mobile users seamlessly roaming in the HetNets environment. Supporting ubiquitous connectivity and enabling very good quality of rich mobile services anywhere and anytime is highly challenging, mostly due to the heterogeneity of the selection criteria, such as: application requirements (e.g., voice, video, data, etc.); different device types and with various capabilities (e.g., smartphones, netbooks, laptops, etc.); multiple overlapping networks using diverse technologies (e.g., Wireless Local Area Networks (IEEE 802.11), Cellular Networks Long Term Evolution (LTE), etc.) and different user preferences. In fact, the mobile users are facing a complex decision when they need to dynamically select the best value network to connect to in order to get the “Always Best Experience”. This thesis presents three major contributions to solve the problem described above: 1) The Location-based Network Prediction mechanism in heterogeneous wireless networks (LNP) provides a shortlist of best available networks to the mobile user based on his location, history record and routing plan; 2) Reputation-oriented Access Network Selection mechanism (RANS) selects the best reputation network from the available networks for the mobile user based on the best trade-off between QoS, energy consumptions and monetary cost. The network reputation is defined based on previous user-network interaction, and consequent user experience with the network. 3) Network Reputation-based Quality Optimization of Video Delivery in heterogeneous networks (NRQOVD) makes use of a reputation mechanism to enhance the video content quality via multipath delivery or delivery adaptation

Distributed Cloud Gaming Pipeline

Since the rise of Cloud infrastructures and the increased accessibility to platforms capable of playing video games, Gaming as a Service (GaaS) has been growing steadily. The aim in this field is to give the players the possibility to play video games anytime anywhere on any device through a streaming service. A lot of effort is being put in the research of new methods to overcome the limits of current Game Engines, built as monolithic entities, and of the QoS of the streaming. This TFM aims to address the former problem by researching and implementing (a part of) an alternative to the monolithic architecture, focused on splitting the engine into independent services. These would then be able to be distributed along the whole cloud continuum depending on the required QoS