Design and evaluation of a DASH-compliant second screen video player for live events in mobile scenarios

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

QoE-centric management of advanced multimedia services

Over the last years, multimedia content has become more prominent than ever. Particularly, video streaming is responsible for more than a half of the total global bandwidth consumption on the Internet. As the original Internet was not designed to deliver such real-time, bandwidth-consuming applications, a serious challenge is posed on how to efficiently provide the best service to the users. This requires a shift in the classical approach used to deliver multimedia content, from a pure Quality of Service (QoS) to a full Quality of Experience (QoE) perspective. While QoS parameters are mainly related to low-level network aspects, the QoE reflects how the end-users perceive a particular multimedia service. As the relationship between QoS parameters and QoE is far from linear, a classical QoS-centric delivery is not able to fully optimize the quality as perceived by the users. This paper provides an overview of the main challenges this PhD aims to tackle in the field of end-to-end QoE optimization of video streaming services and, more precisely, of HTTP Adaptive Streaming (HAS) solutions, which are quickly becoming the de facto standard for video delivery over the Internet