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Cost-Efficient Video On Demand (VOD) Streaming Using Cloud Services Cost-Efficient Video On Demand (VOD) Streaming Using Cloud Services



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. Specifically, we study the impact factors on video transcoding in the cloud, and then we develop a model to trade-off 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 significant cost to the stream providers that rely on cloud services. Furthermore, pre-transcoding is proven to be inefficient 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 partialpre-transcoding methods operate at different granularity levels to capture different 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 efficacy of our proposed methods, specifically, 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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Content Delivery Networks enables the readers to understand the basics, to identify the underlying technology, to summarize their knowledge on concepts, ideas, principles and various paradigms which span on broad CDNs areas. Therefore, aspects of CDNs in terms of basics, design process, practice, techniques, performances, platforms, applications, and experimental results have been presented in a proper order. Fundamental methods, initiatives, significant research results, as well as references for further study have also been provided. Comparison of different design and development approaches are described at the appropriate places so that new researchers as well as advanced practitioners can use the CDNs evaluation as a research roadmap. All the contributions have been reviewed, edited, processed, and placed in the appropriate order to maintain consistency so that any reader irrespective of their level of knowledge and technological skills in CDNs would get the most out of it. The book is organized into three parts, namely, Part I: CDN Fundamentals; Part II: CDN Modeling and Performance; and Part III: Advanced CDN Platforms and Applications. The organization ensures the smooth flow of material as successive chapters build on prior ones.
Multi-version VoD providers either store multiple versions of the same video or transcode video to multiple versions in real-time to offer multiple-bitrate streaming services to heterogeneous clients. However, this could incur tremendous storage cost or transcoding computation cost. There have been some works regarding trading-off between transcoding and storing whole videos, but they did not take into account video segmentation and internal popularity. As a result, they were not cost-efficient. This paper introduces video segmentation and proposes a segment-based storage and transcoding trade-off strategy for multi-version VoD systems in the cloud. First, we split each video into multiple segments depending on the video internal popularity. Second, we describe the transcoding relationships among versions using a transcoding weighted graph, which can be used to calculate the version-aware transcoding cost from one version to another. Third, we take the video segmentation, version-aware transcoding weighted graph, and video internal popularity into account to propose a storage and transcoding trade-off strategy, which stores multiple versions of popular segments and transcodes unpopular segments. We then formulate it as an optimization problem and present a heuristic divideand- conquer algorithm to get an approximate optimal solution. Finally, we conduct extensive simulations to evaluate the solution; the results show that it can significantly lower the storage and transcoding cost of multi-version VoD systems.
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