Digital recording of performing arts: formats and conversion

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Statistical framework for video decoding complexity modeling and prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding

Improving The Efficiency Of Video Transmission In Computer Networks

In-depth examination of current techniques for enhancing the efficiency of video transmission over digital networks is provided in this study. Due to the growing need for high-quality video content, optimizing video transmission is an important area of research. This review categorizes and in-depth examines a range of methods proposed in the literature to enhance video transmission effectiveness. ABR, DNN architecture, adaptive streaming, Quality of Service (QoS), error resilience, congestion control, video compression, and hardware acceleration for video provisioning are just a few of the cutting-edge techniques that are covered in the discussion, which ranges from the more traditional to the cutting-edge. This essay provides a methodical evaluation of the numerous tactics that are available, along with an analysis of their guiding principles, advantages, and disadvantages. The paper also offers a comparative analysis of various approaches, highlighting trends, gaps, and potential future research directions in this crucial domain, all of which help to create more efficient video compression and transmission paradigms in computer networks

Exploring Energy Consumption Issues for video Streaming in Mobile Devices: a Review

The proliferation of high-end mobile devices, such as smart phones, tablets, together have gained the popularity of multimedia streaming among the user. It is found from various studies and survey that at end of 2020 mobile devices will increase drastically and Mobile video streaming will also grow rapidly than overall average mobile traffic. The streaming application in Smartphone heavily depends on the wireless network activities substantially amount of data transfer server to the client. Because of very high energy requirement of data transmitted in wireless interface for video streaming application considered as most energy consuming application. Therefore to optimize the battery USAge of mobile device during video streaming it is essential to understand the various video streaming techniques and there energy consumption issues in different environment. In this paper we explore energy consumption in mobile device while experiencing video streaming and examine the solution that has been discussed in various research to improve the energy consumption during video streaming in mobile devices . We classify the investigation on a different layer of internet protocol stack they utilize and also compare them and provide proof of fact that already exist in modern Smartphone as energy saving mechanism

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

Enhancement of Adaptive Forward Error Correction Mechanism for Video Transmission Over Wireless Local Area Network

Video transmission over the wireless network faces many challenges. The most critical challenge is related to packet loss. To overcome the problem of packet loss, Forward Error Correction is used by adding extra packets known as redundant packet or parity packet. Currently, FEC mechanisms have been adopted together with Automatic Repeat reQuest (ARQ) mechanism to overcome packet losses and avoid network congestion in various wireless network conditions. The number of FEC packets need to be generated effectively because wireless network usually has varying network conditions. In the current Adaptive FEC mechanism, the FEC packets are decided by the average queue length and average packet retransmission times. The Adaptive FEC mechanisms have been proposed to suit the network condition by generating FEC packets adaptively in the wireless network. However, the current Adaptive FEC mechanism has some major drawbacks such as the reduction of recovery performance which injects too many excessive FEC packets into the network. This is not flexible enough to adapt with varying wireless network condition. Therefore, the enhancement of Adaptive FEC mechanism (AFEC) known as Enhanced Adaptive FEC (EnAFEC) has been proposed. The aim is to improve recovery performance on the current Adaptive FEC mechanism by injecting FEC packets dynamically based on varying wireless network conditions. The EnAFEC mechanism is implemented in the simulation environment using Network Simulator 2 (NS-2). Performance evaluations are also carried out. The EnAFEC was tested with the random uniform error model. The results from experiments and performance analyses showed that EnAFEC mechanism outperformed the other Adaptive FEC mechanism in terms of recovery efficiency. Based on the findings, the optimal amount of FEC generated by EnAFEC mechanism can recover high packet loss and produce good video quality

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms