In cellular networks, the proximity devices may share files directly without going through the e NBs, which is called Device-to-Device communications(D2D). It has been considered as a potential technological component...In cellular networks, the proximity devices may share files directly without going through the e NBs, which is called Device-to-Device communications(D2D). It has been considered as a potential technological component for the next generation of communication. In this paper, we investigate a novel framework to distribute video files from some other proximity devices through users' media cloud assisted D2 D communication. The main contributions of this work lie in: 1) Providing an efficient algorithm Media Cloud Cluster Selecting Scheme(MCCSS) to achieve the reasonable cluster; 2) Distributing the optimum updating files to the cluster heads, in order to minimize the expected D2 D communication transmission hop for files; 3) Proposing a minimum the hop method, which can ensure the user obtain required file as soon as possible. Extensive simulation results have demonstrated the efficiency of the proposed scheme.展开更多
Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud comput...Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud computing to better host media servic- es. On the other hand, machine learning techniques have been successfully applied in a variety of multimedia applications as well as a list of infrastructure and platform services. In this article, we present a tutorial survey on the way of using machine learning techniques to address the emerging challenges in the infrastructure and platform layer of media cloud. Specifically, we begin with a review on the basic concepts of various machine learning techniques. Then, we examine the system architecture of media cloud, focusing on the functionalities in the infrastructure and platform layer. For each of these function and its corresponding challenge, we further illustrate the adoptable machine learning based approaches. Finally, we present an outlook on the open issues in this intersectional domain. The objective of this article is to provide a quick reference to inspire the researchers from either machine learning or media cloud area.展开更多
Recent years have witnessed the blooming of mobile devices and applications.Mobile users not only expect a faster broadband connection to access Internet and interact with each other,but also demand ubiquitous enjoyin...Recent years have witnessed the blooming of mobile devices and applications.Mobile users not only expect a faster broadband connection to access Internet and interact with each other,but also demand ubiquitous enjoying of video contents and services.However,this trend is seriously hindered by the fact that mobile devices have limited resources in terms of computation,storage,展开更多
In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personali...In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personalized and suggest that virtualization and tighter cross-layer communication allows for convergence and seamless transition of services. These are also creating new and never-before seen ways of developing and delivering personalized any-media services. We discuss current paradigms for implementing cloud-based any-media services that generate revenue. Future research topics and requirements for evolving network and service elements are also discussed.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61322104,61571240)the State Key Development Program of Basic Research of China(2013CB329005)+3 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe University Natural Science Research Foundation of Anhui Province(No.KJ2015A105,No.KJ2015A092)The open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications),Ministry of Education(NYKL201509)The open research fund of the State Key Laboratory of Integrated Services Networks,Xidian University(ISN17-04)
文摘In cellular networks, the proximity devices may share files directly without going through the e NBs, which is called Device-to-Device communications(D2D). It has been considered as a potential technological component for the next generation of communication. In this paper, we investigate a novel framework to distribute video files from some other proximity devices through users' media cloud assisted D2 D communication. The main contributions of this work lie in: 1) Providing an efficient algorithm Media Cloud Cluster Selecting Scheme(MCCSS) to achieve the reasonable cluster; 2) Distributing the optimum updating files to the cluster heads, in order to minimize the expected D2 D communication transmission hop for files; 3) Proposing a minimum the hop method, which can ensure the user obtain required file as soon as possible. Extensive simulation results have demonstrated the efficiency of the proposed scheme.
文摘Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud computing to better host media servic- es. On the other hand, machine learning techniques have been successfully applied in a variety of multimedia applications as well as a list of infrastructure and platform services. In this article, we present a tutorial survey on the way of using machine learning techniques to address the emerging challenges in the infrastructure and platform layer of media cloud. Specifically, we begin with a review on the basic concepts of various machine learning techniques. Then, we examine the system architecture of media cloud, focusing on the functionalities in the infrastructure and platform layer. For each of these function and its corresponding challenge, we further illustrate the adoptable machine learning based approaches. Finally, we present an outlook on the open issues in this intersectional domain. The objective of this article is to provide a quick reference to inspire the researchers from either machine learning or media cloud area.
文摘Recent years have witnessed the blooming of mobile devices and applications.Mobile users not only expect a faster broadband connection to access Internet and interact with each other,but also demand ubiquitous enjoying of video contents and services.However,this trend is seriously hindered by the fact that mobile devices have limited resources in terms of computation,storage,
文摘In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personalized and suggest that virtualization and tighter cross-layer communication allows for convergence and seamless transition of services. These are also creating new and never-before seen ways of developing and delivering personalized any-media services. We discuss current paradigms for implementing cloud-based any-media services that generate revenue. Future research topics and requirements for evolving network and service elements are also discussed.