User Centric Content Management System for Open IPTV Over SNS (ICTC2012)

Coupled schemes between service-oriented architecture (SOA) and Web 2.0 have recently been researched. Web-based content providers and telecommunications company (Telecom) based Internet protocol television (IPTV) providers have struggled against each other to accommodate more three-screen service subscribers. Since the advent of Web 2.0, more abundant reproduced content can be circulated. However, because according to increasing device's resolution and content formats IPTV providers transcode content in advance, network bandwidth, storage and operation costs for content management systems (CMSs) are wasted. In this paper, we present a user centric CMS for open IPTV, which integrates SOA and Web 2.0. Considering content popularity based on a Zipf-like distribution to solve these problems, we analyze the performance between the user centric CMS and the conventional Web syndication system for normalized costs. Based on the user centric CMS, we implement a social Web TV with device-aware function, which can aggregate, transcode, and deploy content over social networking service (SNS) independently.Comment: 10 pages, 17 figures, An earlier version of this paper was awarded as best paper at the IEEE International Conference on ICT Convergence (ICTC), Jeju, Korea, October 201

Content Personalization and Adaptation for Three-Screen Services

Three-screen services provide the right solution for consumers to access rich multimedia resources by any device, anytime and anywhere. In this paper, we describe a prototype system of content personalization and adaptation for three-screen services. The system continuously acquires content from TV broadcast feeds, indexes and adapts the content for users according to their interests defined in preference profiles. Automatically compiled segments of content can be rendered on a variety of devices that the customers prefer to facilitate a smoother video consuming experience. Simulation results show that the proposed content analysis modules, including shot boundary detection, anchorperson detection, and multimodal story segmentation are effective. The resulting personalized content is suitable for consumption on devices with limited input capabilities