THESIS
2017
Abstract
To support a phenomenal uptick in user demand for video live streaming, the video service
providers have to significantly scale up their network resources. To cost-effectively address
this problem, we consider the fog computing paradigm, which deploys the computational
resource closer to end users for better efficiency and responsiveness. We realize the fog
computing features on commercial off-the-shelf devices by implementing 2 schemes. First,
we implement FogStream, a scalable live streaming system that enables fog devices, such
as routers, to deliver the streaming. Also, we develop Mog, which allows smartphones to
share the streaming to its neighbors through Wi-Fi. Experimental study on both schemes
shows that they can effectively deliver the live streaming and save the bandw...[
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To support a phenomenal uptick in user demand for video live streaming, the video service
providers have to significantly scale up their network resources. To cost-effectively address
this problem, we consider the fog computing paradigm, which deploys the computational
resource closer to end users for better efficiency and responsiveness. We realize the fog
computing features on commercial off-the-shelf devices by implementing 2 schemes. First,
we implement FogStream, a scalable live streaming system that enables fog devices, such
as routers, to deliver the streaming. Also, we develop Mog, which allows smartphones to
share the streaming to its neighbors through Wi-Fi. Experimental study on both schemes
shows that they can effectively deliver the live streaming and save the bandwidth in realistic
scenarios.
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