Data-driven Approaches for Social Video Distribution
This work addresses video distribution problems for users in social networks, but it appears incremental as it builds on existing concepts without introducing a new paradigm.
The paper tackles the challenges of delivering user-generated video content in social networks by proposing a data-driven framework that identifies unique characteristics of social video access and propagation to enhance user experience.
The Internet has recently witnessed the convergence of online social network services and online video services: users import videos from content sharing sites, and propagate them along the social connections by re-sharing them. Such social behaviors have dramatically reshaped how videos are disseminated, and the users are now actively engaged to be part of the social ecosystem, rather than being passively consumers. Despite the increasingly abundant bandwidth and computation resources, the ever increasing data volume of user generated video content and the boundless coverage of socialized sharing have presented unprecedented challenges. In this paper, we first presents the challenges in social-aware video delivery. Then, we present a principal framework for data-driven social video delivery approaches. Moreover, we identify the unique characteristics of social-aware video access and the social content propagation, and closely reveal the design of individual modules and their integration towards enhancing users' experience in the social network context.