Abstract
Universities are continually pursuing ways to adapt their educational practices, looking to develop new learning culture that encourages creativity and active engagement. The adoption of augmented lecture podcast scenarios, based on a content-centric paradigm, looks a promising way to reach a new level of interactivity. This paper analyzes the main advantages provided by this innovative approach comparing it with the traditional podcasting developing model. Additionally, it proposes possible business models based on content distribution and augmentation, to enable wider exploitation of the approach within university environments. © 2012 Infonomics Society.

Abstract
This paper proposes a framework for a virtual classroom application based on a Virtual Collaboration System (VCS), which is being developed under the VISNET II Network of Excellence (NoE)(1), and discusses adaptation technologies that enable seamless access to classroom sessions while intellectual property and digital rights are managed. The proposed virtual classroom framework enables academic institutions to conduct their collaborative lecture series, to which registered students will be able to attend remotely and interactively over the Internet. Furthermore, the general public may also follow the classroom sessions under certain restrictions imposed by the participating institutions. In order to facilitate seamless access to a heterogeneous audience that is composed of users with various preferences and privileges accessing the classroom sessions over different network infrastructures and using terminal devices with diverse capabilities, context-aware content adaptation is required to meet constraints imposed by the usage context and enhance the quality of the user experience. Thus, this paper describes the concepts and functionalities of a context-aware content adaptation platform, that suits the requirements of such multimedia application scenarios. This platform is able to consume low-level contextual information to infer higher-level contexts, and thus decide the need for and type of adaptation operations to be performed upon the content. In this way, it is aimed to meet usage. constraints while also satisfying restrictions imposed by the Digital Rights Management (DRM) to govern the use of protected content.

Abstract
The use of formal probabilistic modelling together with advanced statistical inference in communications engineering, has the potential of providing a powerful workbench for deriving conclusions about the behaviour of systems and sources of data and therefore developing innovative and efficient control and management strategies of communication systems. Targeting the efficient dimensioning of systems and resources in order to make possible the provision of high quality and larger-choice services to the end-users, this paper proposes the use of a Bayesian framework in video broadband communications. In the context of the combined transmission of several good-quality video sources, such as a digital television broadcast service, using VBR video encoding algorithms and based on prior beliefs and statistical models, an intelligent multiplexer is capable of predicting the probable amount of free space and fill it with extra services or increase the quality of the transmitted channels.

Abstract

Abstract
In this paper we present an experimental approach for QoS-aware and bandwidth-efficient transmission of multimedia sources over ATM networks. VER video sources are statistically multiplexed at the application level and the UPC parameters of a single connection for the aggregate traffic are dynamically renegotiated with the network during the session lifetime, A statistical multiplexer computes the required bandwidth for the aggregate traffic and dynamically assigns bandwidth according to the performances requested and network resources availability. A dynamic UPC manager using feedback information sent by the network, may initiate a renegotiation of traffic parameters upon receiving a request from the statistical multiplexer. Performing the connection admission control procedures for the aggregate traffic, allows significant reductions in the value of the total peak rate when compared to the sum of peak rates of individual connections. It also reduces the burstiness of the flow submitted to the network, thus increasing the lifetime of each set of UPC parameters.

Abstract
Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer's focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user's visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.