An efficient scheduling multimedia transcoding method for DASH streaming in cloud environment

As a result of technological evolution, streaming service providers have been dealing with the problem of delivery multimedia content to the diversity of devices with different resolutions. This issue can be solved by using dynamic adaptive streaming over hypertext (DASH) transfer protocol. However, a transcoding job in DASH requires a lot of computation resource which could lead to delaying the starting of multimedia streaming. Recently, new studies have addressed novel scheduling methods on video transcoding, but those research did not solve the problem entirely, such as the solution did not concern server performance or speed connection between a server and its requested users. Moreover, the load and speed connection status of the data servers is often unstable, leading to increasing the starting delay. So in this article, we solve such problem by modeling transcoding jobs in the form of an optimization problem and propose an algorithm to find an optimal schedule to transcode video source files. In which, we use moving average method to find average points for a short period to deal with server state changes. In the experiment, we implement our proposed method with DASH to demonstrate the effectiveness of the optimization scheduling method. In the system, we create several servers running on the Docker platform to simulate a cloud environment. Experimental results show that our methodology reduces the time of the transcoding process up to 30% compared to existing research.