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International audience

International audience

International audience

RÉSUMÉ. Cet article est une étude de l’évolution du nombre de vues des contenus dans You- Tube. Nous proposons dans un premier temps plusieurs modèles inspirés de l’économie et de la biologie pour caractériser les courbes d’évolution des nombres de vues des vidéos. Dans un deuxième temps, nous proposons une méthode automatique de classification de ces courbes en les associant à l’un des différents modèles suggérés. Nous montrons, sur un large ensemble de données, que 90% des vidéos peuvent être associées à l’un de ces modèles avec une erreur moyenne inférieure à 5%. Une étude empirique est menée au sujet de l’impact de la popularité et des catégories de vidéos sur l’évolution des nombres de vues. Enfin, cette classification est utilisée dans un exemple de méthode de prédiction de la popularité des vidéos.

International audience

International audience

International audience

The past decade has witnessed a significant improvement in speaker recognition SR technology in terms of performance with the introduction of the i-vectors framework. Despite these advances, the performance of SR systems considerably suffers in the presence of acoustic nuisances and variabilities. In this paper, we develop a data-driven nuisance compensation technique in the i-vector space without referring to the effects of the targeted nuisances in the temporal domain. This approach is nonparametric as it does not suppose a specific relationship between a “good” version of an i-vector and its corrupted version. Instead, our algorithm models directly the joint distribution of both representations the good i-vector and its corrupted version and takes advantage of the reproducibility of acoustic corruptions to generate the corrupted i-vectors. We then build an MMSE estimator that computes an improved version of a corrupted test i-vector, given this joint distribution. Experiments are carried out on NIST SRE 2010 and speakers in the wild databases where the proposed algorithm is used to deal with additive noise and short utterances. Our technique is shown to be efficient, improving the baseline system performance in terms of equal-error rate by up to 70% when used on known test noises and up to 65% in the context of unseen noises using a generic model. It was also proven efficient in the context of duration mismatch reaching up to 40% of relative improvement when used on short utterances using multiple models corresponding to different durations and up to 36% when used on arbitrary duration test segments.

The demand for multimedia services in mobile communication is increasing day by day due to the proliferation of end devices. To overcome the future needs of data communication on mobile devices, the $ 3^{rd} $ Generation Partnership Project (3GPP) has introduced a new technology which is known as Long Term Evolution (LTE) UMTS. In this paper, we study video streaming behavior on the LTE network. For this purpose, we use the Vienna LTE System Level Simulator which implements network features that correspond to our goal. We consider the parameters that may affect the users experience on a network and define ten scenarios for our simulations. We finally search for methods to fit the video streaming data traffic on the LTE network and exhibit its long range dependence.

Mobile edge caching will represent a 5G service able to improve the quality of experience of users served on mobile networks. Video streaming is the candidate traffic category which will leverage such service. In turn, the availability of rate-adaptive video playout mechanisms on board of mobile terminals will improve the video-streaming experience and offer a novel dimension to optimize caching policies in a site-dependent fashion. In this paper we present new rate-aware caching schemes able to optimize the cache composition both versus popularity of video contents and the aggregated users' video rate demand. Several algorithms are proposed and compared against their rate-agnostic counterparts. Extensive numerical results provide evaluation of the relative advantage of this new class of caching policies.

HTTP Adaptive Streaming is able to dynamically match video quality to variable network conditions. This is a key feature for multimedia delivery when quality of service cannot be granted network-wide. For instance, the end-to-end throughput towards mobile terminals may suffer short term fluctuations due to fading. Hence, robust bitrate adaptation schemes become crucial in order to avoid degraded video reproduction. The objective, in this context, is to control the filling level of the playback buffer, maximize the video quality, and avoid unnecessary quality variations which may also impair the perceived quality of experience. In this work we study bitrate adaptation algorithms leveraging on Backward-Shifted Coding (BSC), a scalable video coding scheme able to cope with the effects of end-to-end throughput fluctuations. We have proposed a new adaptation scheme able to balance video rate smoothness and high network capacity utilization. Both the throughput-based and buffer-based variants of such scheme have been designed. Extensive simulations using synthetic and real-world video traffic traces show that the proposed solution performs remarkably well even under challenging network conditions.

This paper proposes a new prediction process to explain and predicts popularity evolution of YouTube videos. We exploit prior study on the classification of YouTube videos in order to predict the evolution of videos' view-count. This classification allows to identify important factors of the observed popularity dynamics. In particular, we use this classification as filtering method allowing to identify the factors responsible for this popularity evolution. Results given by extensive experiments show that the proposed prediction process is able to reduce the average prediction errors compared to a state-of-the-art baseline model. We also evaluate the impact of adding popularity criteria in the classification.