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

Machine learning adaptive computational capacity prediction for dynamic resource management in C-RAN

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN)environments is a challenging problem because it has to account simultaneously for throughput, latency,power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at thebaseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overallQuality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instan-tiated according to the required computational capacity (RCC). In this paper, a new strategy for DynamicResource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML)techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: supportvector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higherthan the predicted computational capacity (PCC). To further improve, two new strategies are proposed andtested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting(DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectivelyThis work was supported in part by the Spanish ministry of science through the project CRIN-5G (RTI2018-099880-B-C32) withERDF (European Regional Development Fund) and in part by the UPC through COST CA15104 IRACON EU Project and theFPI-UPC-2018 Grant.Peer ReviewedPostprint (published version

TCM, TTCM, BICM and BICM-ID Assisted MMSE Multi-User Detected SDMA-OFDM Using Walsh-Hadamard Spreading

Space Division Multiple Access (SDMA) aided Orthogonal Frequency Division Multiplexing (OFDM) systems assisted by efficient Multi-User Detection (MUD) techniques have recently attracted intensive research interests. Forward Error Correction (FEC) schemes and frequency-domain spreading techniques can be efficiently amalgamated with SDMA-OFDM systems for the sake of improving the achievable performance. In this contribution a Coded Modulation (CM) assisted and Minimum Mean-Square Error (MMSE) multi-user detected SDMA-OFDM system combined with Walsh-Hadamard-Transform-Spreading (WHTS) across a number of subcarriers is proposed. The various CM schemes used are Trellis Coded Modulation (TCM), Turbo TCM (TTCM), Bit-Interleaved Coded Modulation (BICM) and Iteratively Decoded BICM (BICM-ID), which constitute bandwidth efficient schemes that combine the functions of coding and modulation. Invoking the WHTS technique is capable of further improving the average Bit Error Rate (BER) performance of the CM-SDMA-OFDM system, since the bursty error effects imposed by the frequency-domain fading encountered are spread over the entire WHT block length, therefore increasing the chances of correcting the transmission errors by the CM decoders

Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

Digital Rights Management and Consumer Acceptability: A Multi-Disciplinary Discussion of Consumer Concerns and Expectations

The INDICARE project – the Informed Dialogue about Consumer Acceptability of DRM Solutions in Europe – has been set up to raise awareness about consumer and user issues of Digital Rights Management (DRM) solutions. One of the main goals of the INDICARE project is to contribute to the consensus-building among multiple players with heterogeneous interests in the digital environment. To promote this process and to contribute to the creation of a common level of understanding is the aim of the present report. It provides an overview of consumer concerns and expectations regarding DRMs, and discusses the findings from a social, legal, technical and business perspective. A general overview of the existing EC initiatives shows that questions of consumer acceptability of DRM have only recently begun to draw wider attention. A review of the relevant statements, studies and reports confirms that awareness of consumer concerns is still at a low level. Five major categories of concerns have been distinguished so far: (1) fair conditions of use and access to digital content, (2) privacy, (3) interoperability, (4) transparency and (5) various aspects of consumer friendliness. From the legal point of view, many of the identified issues go beyond the scope of copyright law, i.e. the field of law where DRM was traditionally discussed. Often they are a matter of general or sector-specific consumer protection law. Furthermore, it is still unclear to what extent technology and an appropriate design of technical solutions can provide an answer to some of the concerns of consumers. One goal of the technical chapter was exactly to highlight some of these technical possibilities. Finally, it is shown that consumer acceptability of DRM is important for the economic success of different business models based on DRM. Fair and responsive DRM design can be a profitable strategy, however DRM-free alternatives do exist too.Digital Rights Management; consumers; Intellectual property; business models

Multiparty multilevel watermarking protocol for digital secondary market based on iris recognition technology

Background: In order to design secure digital right management architecture between different producers and different consumers, this paper proposes a multiparty and multilevel watermarking protocol for primary and secondary market. Comparing with the traditional buyer-seller watermarking protocols, this paper makes several outstanding achievements. Method: First of all, this paper extends traditional buyer-seller two-party architecture to multiparty architecture which contains producer, multiply distributors, consumers, etc. Secondly, this paper pays more attention on the security issues, for example, this paper applies iris recognition technology as an advanced security method. Conclusion: Finally, this paper also presents a second-hand market scheme to overcome the copyright issues that may happen in the real world. © 2017 Bentham Science Publishers

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584