This paper describes a design framework for TCPfriendly and media-friendly rate control algorithms for multimedia streaming applications. The idea of this framework is to start from TFRC's (TCP-Friendly Rate Control) transmission rate and then alter this transmission rate so that it tracks the media characteristics of the stream (e.g., bitrate) or other application characteristics like the client buffer fill level. In this way, the media-friendly property of the algorithm is achieved. We give three rules that guide how the TFRC throughput should track the evolution of the stream's media characteristics and remain TCPfriendly in the long term. We also present, as proof of concept, four simple media-friendly and TCP-friendly congestion control algorithms built using the aforementioned framework. These congestion control algorithms are better suited for multimedia streaming applications than traditional TCP congestion control or smooth congestion control algorithms like TFRC. We have performed evaluations of two of the four proposed media-friendly and TCP-friendly congestion control algorithms under various network conditions and validated that they represent viable transport solutions, better than TFRC, for variable bitrate video streams. More specifically, our two media-friendly and TCPfriendly congestion control algorithms maintained a TCP-friendly throughput in the long term in all experiments and avoided an empty buffer at the client side in situations when TFRC could not achieve this.
In this paper we present a way of using the very recent user's browsing history and query history, in order to improve the query suggestions mechanism used by an information retrieval system. In order to collect this kind of data, we have built a Chrome browser plugin that monitors user's web activity and stores that data so that we can create a better personal profile for each user. We then analyzed if future queries submitted by a user to the search engine can be predicted from web pages visited by that user in the past (i.e. his recent browsing history) or from queries submitted by that user in the past (i.e. his recent query history). The contribution of this paper is twofold: a) an evaluation of the relevancy of the user's recent browsing and query history to future queries submitted by this user and b) a method for personalizing the query suggestions offered by the Google search engine. More specifically, we are using the user's personal (browsing) history profile in order to reorder query suggestions offered by the Google search engine (i.e. we move query suggestions more relevant to the user's information need to the front positions in the Google provided query suggestions list).Index Terms-information retrieval, query suggestion, query auto-completion, personalized query history.
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