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We consider the following problem in this paper: A video is encoded as a set of tiles T and is streamed to multiple users via a onehop wireless LAN. Each user selects a region-of-interest (RoI), represented as a subset of T , in the video to watch. The RoI selected by the users may overlap. Each tile may be multicast or unicast. We define the tile assignment problem as: which subset of tiles should be multicast such that every user receives, within a transmission deadline, the subset of tiles pertaining to the RoI the user selected, while minimizing the number of unwanted tiles received by users. We present and evaluate five tile assignment methods. We show that: (i) minimizing transmission delay can lead to significant wasteful reception in the multicast group, (ii) using tile access probability to assign tiles frequently leads to assignments that violate the deadline, and (iii) a fast, greedy, heuristic works well: it performs close to the optimal method and can always find an assignment within the deadline (as long as such assignment exists).

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Abstract. This paper describes a sentence ranking technique using entropy measures, in a multi-document unstructured text summarization application. The method is topic specific and makes use of a simple language independent training framework to calculate entropies of symbol units. The document set is summarized by assigning entropy-based scores to a reduced set of sentences obtained using a graph representation for sentence similarity. The performance is seen to be better than some of the common statistical techniques, when applied on the same data set. Commonly used measures like precision, recall and f-score have been modified and used as a new set of measures for comparing the performance of summarizers. The rationale behind such a modification is also presented. Experimental results are presented to illustrate the relevance of this method in cases where it is difficult to have language specific dictionaries, translators and document-summary pairs for training.