Transcoding resources forecasting and reservation for crowdsourced live streaming

Abstract

During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site. 2019 IEEE.