Video streaming has become ubiquitous and pervasive in usage of the electronic displaying devices. Streaming becomes more challenging when dealing with an enormous number of video streams. Particularly, the challenges lie in streaming types, video transcoding, video storing, and video delivering to users with high satisfaction and low cost for video streaming providers. In this dissertation, we address the challenges and issues encountered in video streaming and cloud-based video streaming. Specically, we study the impact factors on video transcoding in the cloud, and then we develop a model to trade-o between performance and cost of cloud. On the other hand, video streaming providers generally have to store several formats of the same video and stream the appropriate format based on the characteristics of the viewer's device. This approach, called pre-transcoding, incurs a signicant cost to the stream providers that rely on cloud services. Furthermore, pre-transcoding is proven to be inecient due to the long-tail access pattern to video streams. To reduce the incurred cost, we propose to pre-transcode only frequently-accessed videos (called hot videos) and partially pre-transcode others, depending on their hotness degree. Therefore, we need to measure video stream hotness. Accordingly, we first, provide a model to measure the hotness of video streams. Then, we develop methods that operate based on the hotness measure and determine how to pre-transcode videos to minimize the cost of stream providers. The partial pre-transcoding methods operate at dierent granularity levels to capture dierent patterns in accessing videos. Particularly, one of the methods operates faster but cannot partially pre-transcode videos with the non-long-tail access pattern. Experimental results show the ecacy of our proposed methods, specically, when a video stream repository includes a high percentage of Frequently Accessed Video Streams and a high percentage of videos with the non-long-tail accesses pattern.
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