Pre-fetching and Caching in Catch-Up TV Network
Abstract: Nowadays, huge amounts of data are transferred over the Internet. Video content is a vast majority of the data traffic. A part of this data is wasted due to many reasons. Transmitting large amounts of data across a network leads to network congestion and start-up delay of the video playback, which is one of the reasons of data loss. Another reason is the negative effect of impatient users or so-called zappers. This effect is expressed by the unexpected change in user preference during the streaming process of the requested content. In that case, the downloaded data are discarded which can be considered as a waste of network and system resources. One of the solutions to this problem is to develop a system for prefetching and caching, which is used to prepare the expected contents to be ready for any possible request by the users. The improvement in the system is approached by eliminating excess data transfer, and by caching needed parts of the contents. Thus reducing the load on the network and saving the resources. The aim of this study is to investigate user behavior and define user groups as Loyals or Zappers on a scale of a grading system. This is performed by analyzing collected data of user requests and content details to find a way to adjust prefetching and caching system settings based on several factors; user behavior, session length, content length, and content popularity. An individual calculation for each of these factors is done to get the specified results which are shown in graphs to have an overview of the analyzed data, and to extract useful information to reach the goal of this work. All of the studied subjects are contributed to produce an enhanced model of the prefetching and caching system. After demonstrating the results, some variable values can be evaluated from the calculations. These values vary depending on the processed data. That would affect the accuracy of the outcomes.
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