Collaborative Framework of Cloud Transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming

With the rapid development of high-speed Internet access and popularization of high-performance smart devices, past decade has witnessed the great development of crowdsourced live streaming (CLS) service. Transcoding and video distribution are essential in CLS service to guarantee viewer engagement. Large CLS systems gradually migrate their services to multi-cloud platforms. However, highly dynamic viewers' requests influence transcoding and CDN distribution decisions, eventually lead to fluctuation in QoE and increase in operational cost. It is challenging for the CLS system to serve viewer's requests in multi-cloud platforms with fluctuation in cloud transcoding and distribution performance. In this paper, we propose a collaborative framework of cloud transcoding and distribution supporting CLS service. First, we define cost model and QoE model in multi-cloud platforms, comprehensively considering cloud transcoding and distribution. Second, we propose a collaborative cost-efficient approach based on multi-agent decision model. We use a G-Greedy exploration approach to learn what actions to take by exploration and exploitation based on the state of current environment. The trace-driven experiments demonstrate that our proposed approach is cost-efficient and QoE-maintained and can reduce operational cost compared with alternatives (5.37%-21.21%) while maintaining QoE of viewers.