The way computing was done has changed a lot in recent times. Nowadays mobile devices have been the stealing the show. These devices come in different specifications, that any multimedia content that is to be played requires transcoding for better user experiences. Cloud based video services cater to the needs of the end user based on their requirements, through video transcoding. Hence video transcoding plays a very important role in today’s evolving streaming media environment. The major problem with video transcoding is that it consumes a lot of time and impacts seriously on the quality of the output. Transcoding uses the device information to transform the video into the required format and this process is done in a distributed fashion, to speed up the process. This work proposes an Intelligent Video Splitter which uses the Map Reduce algorithm to provide efficiency based on time factor. The important performance metrics including video distortion (VD), video distortion due to frame dependency (FDD) were considered. The results showed that the proposed framework perceptibly outperforms than the prevailing strategies. It provides higher video quality as a result of it introduces less video distortion. In future this method may be extended to supply associate automatic device aware video standards.