Recent years have witnessed an explosion of gamecasting applications in the market, in which game players (or gamers in short) broadcast their game scenes in real-time. Such pioneer applications as YouTube Gaming, Twitch, and Mobcrush have attracted a massive number of online broadcasters, and each of them can attract hundreds or thousands of fellow viewers. The growing number however has created significant challenges to the network and end-devices, particularly considering 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 crowdsourced measurement reveals 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 (MGC). Our framework incorporates a touch-assisted prediction module to extract association rules for gazing pattern prediction and a tile-based optimization module to utilize energy on mobile devices efficiently. Trace-driven simulations illustrate the effectiveness of our framework in terms of energy consumption and streaming quality. Our user study experiments also demonstrate much improved (3%-13%) quality satisfaction than the state-of-the-art solution with similar network resources.

Driven by the advances in personal computing devices and the prevalence of high-speed network accesses, crowdsourced livecast platforms have emerged in recent years, through which numerous broadcasters lively stream their video content to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are highly heterogeneous in terms of the network/system configurations and, therefore, the generated video quality, which calls for massive encoding and transcoding in order to unify the video sources and serve multiple quality versions to viewers with different configurations. On the other hand, with the rapid evolution in the hardware industry, high-performance processors become mainstream in personal computer market. More end devices can easily transcode high-quality videos in realtime. We witness huge computational resource among the massive fellow viewers that could potentially be used for transcoding. In this paper, we propose CrowdTranscoding, a novel framework for crowdsourced livecast systems that offloads the transcoding assignment to the massive viewers. We identify that the key challenges in CrowdTranscoding are to detect qualified stable viewers and to properly assign them to the source channels. We put forward a viewer crowdsourcing transcode scheduler to smartly schedule the workload assignment. Our solution has been evaluated under diverse viewer/channel conditions as well as different parameter settings. The trace-driven simulation confirms the superiority of CrowdTranscoder, while our PlanetLab-based and real world end-viewer experiments show the practical performance of our approach, which also give hint to the further enhancement.

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.

Driven by the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, Crowdsourced Live Streaming (CLS) platforms have emerged in recent years, through which numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are highly heterogenous in terms of the network/system configurations and therefore the generated video quality, which calls for massive encoding and transcoding in order to unify the video sources and serve multiple quality versions to viewers with different configurations. On the other hand, with the rapid evolution in the hardware industry, high performance processors (e.g., Intel Core i7-4790K CPU) become mainstream in personal computer market. More end devices can easily transcode high quality videos in realtime. We witness huge computational resource among the massive fellow viewers that could potentially be used for transcoding. In this paper, inspired by fog computing, we propose Crowd-Transcoding, a novel framework for CLS systems that offloads the transcoding assignment to the massive viewers. We identify that the key challenges in CrowdTranscoding are to detect qualified stable viewers and to properly assign them to the source channels. We put forward Viewer Crowdsourcing Transcode Scheduler (VCTS) to smartly schedule the workload assignment. Our solution has been evaluated under diverse viewer/channel conditions as well as different parameter settings. The trace-driven simulation confirms the superiority of CrowdTranscoder, while our PlanetLab-based and real world end-viewer experiments show the practical performance of our approach, which also give hint to the further enhancement.

With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network/system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability/pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration.