Proactive Caching of Music Videos based on Audio Features, Mood, and Genre

The preferred channel for listening to music is shifting towards the Internet and especially to mobile networks. Here, the overall traffic is predicted to grow by 45% annually till 2021. However, the resulting increase in network traffic challenges mobile operators. As a result, methods are researched to decrease costly transit traffic and the traffic load inside operator networks using in-network and client-side caching. Additionally to traditional reactive caching, recent works show that proactive caching increases cache efficiency. Thus, in this work, a mobile network using proactive caching is assumed. As music represents the most popular content category on YouTube, this work focuses on studying the potential of proactively caching content of this particular category using a YouTube trace containing over 4 million music video user sessions. The contribution of this work is threefold: First, music content-specific user behavior is derived and audio features of the content are analyzed. Second, using these audio features, genre and mood classifiers are compared in order to guide the design of new proactive caching policies. Third, a novel trace-based evaluation methodology for music-specific proactive in-network caching is proposed and used to evaluate novel proactive caching policies to serve either an aggregate of users or individual clients.