Exploring Viewer Gazing Patterns for Touch-Based Mobile Gamecasting

Recent years have witnessed an explosion of gamecasting applications, in which game players (or gamers in short) broadcast game playthroughs by their personal devices in real time. Such pioneer platforms, such as YouTube Gaming, Twitch, and Mobcrush, have attracted a massive number of online broadcasters, and each of them can have hundreds or thousands of fellow viewers. The growing number, however, has created significant challenges to the network and end-devices, particularly considering that bandwidth- and battery-limited smartphones or tablets are becoming dominating for both gamers and viewers. Yet the unique touch operations of the mobile interface offer opportunities, too. In this paper, our measurements based on the real traces from gamers and viewers reveal that strong associations exist between the gamers' touch interactions and the viewers' gazing patterns. Motivated by this, we present a novel interaction-aware optimization framework to improve the energy utilization and stream quality for mobile gamecasting. Our framework incorporates a touch-assisted prediction module to extract association rules for gazing pattern prediction and a tilebased optimization module to utilize energy on mobile devices efficiently. Trace-driven simulations illustrate the effectiveness of our framework in terms of energy consumption and stream quality. Our user study experiments also demonstrate much improved (3%-13%) quality satisfaction over the state-of-the-art solution with similar network resources.