Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents

This paper proposes a control scheme for the quality-fair delivery of several encoded video streams to mobile users sharing a common wireless resource. Video quality fairness, as well as similar delivery delays are targeted among streams. The proposed controller is implemented within some aggregator located near the bottleneck of the network. The transmission rate among streams is adapted based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server (fully centralized solution), or directly evaluated by each server in a distributed way (partially distributed solution). Each encoding rate target is adjusted for each stream independently based on the corresponding buffer level or buffering delay in the aggregator. Communication delays between the servers and the aggregator are taken into account. The transmission and encoding rate control problems are studied with a control-theoretic perspective. The system is described with a multi-input multi-output model. Proportional Integral (PI) controllers are used to adjust the video quality and control the aggregator buffer levels. The system equilibrium and stability properties are studied. This provides guidelines for choosing the parameters of the PI controllers. Experimental results show the convergence of the proposed control system and demonstrate the improvement in video quality fairness compared to a classical transmission rate fair streaming solution and to a utility max-min fair approach

Guaranteed characterization of exact non-asymptotic confidence regions

accepté à AutomaticaIn parameter estimation, it is often desirable to supplement the estimates with an assessment of their quality. A new family of methods proposed by Campi et al. for this purpose is particularly attractive, as it makes it possible to obtain exact, non-asymptotic con dence regions under mild assumptions on the noise distribution. A bottleneck of this approach, however, is the numerical characterization of these con dence regions. So far, it has been carried out by gridding, which provides no guarantee as to its results and is only applicable to low dimensional spaces. This paper shows how interval analysis can contribute to removing this bottleneck

Guaranteed characterization of exact confidence regions for FIR models under mild assumptions on the noise via interval analysis

International audienceSPS is one of the two methods proposed recently by Campi et al. to obtain exact, non-asymptotic confidence regions for parameter estimates under mild assumptions on the noise distribution. It does not require the measurement noise to be Gaussian (or to have any other known distribution for that matter). The numerical characterization of the resulting confidence regions is far from trivial, however, and has only be carried out so far on very low-dimensional problems via methods that could not guarantee their results and could not be extended to large-scale problems because of their intrinsic complexity. The aim of the present paper is to show how interval analysis can contribute to a guaranteed characterization of exact confidence regions in large-scale problems. The application considered is the estimation of the parameters of finite-impulse response (FIR) models. The structure of the problem makes it possible to define a very efficient specific contractor, allowing the treatement of models with a large number of parameters, as is the rule for FIR models, and thus escaping the curse of dimensionality that often plagues interval methods

Guaranteed characterization of exact non-asymptotic confidence regions as defined by LSCR and SPS

International audienceIn parameter estimation, it is often desirable to supplement the estimates with an assessment of their quality. A new family of methods proposed by Campi et al. for this purpose is particularly attractive, as it makes it possible to obtain exact, non-asymptotic confidence regions under mild assumptions on the noise distribution. A bottleneck of this approach, however, is the numerical characterization of these confidence regions. So far, it has been carried out by gridding, which provides no guarantee as to its results and is only applicable to low dimensional spaces. This paper shows how interval analysis can contribute to removing this bottleneck

Evaluation of the distance spectrum of variable-length finite-state codes

International audienceThe class of variable-length finite-state joint source-channel codes is defined and a polynomial complexity algorithm for the evaluation of their distance spectrum presented. Issues in truncating the spectrum to a finite number of (possibly approximate) terms are discussed and illustrated by experimental results

Caractérisation garantie de régions de confiance non-asymptotiques

National audienceRécemment, Campi et al. ont proposé deux nouvelles familles de méthodes (LSCR et SPS) pour caractériser la précision de l'estimation de la valeur des paramètres de modèles dont la sortie est non-linéaires en ces paramètres à partir de mesures bruitées. Ces méthodes permettent d'obtenir des régions de confiance exactes et non-asymptotiques, en faisant uniquement l'hypothèque que les échantillons de bruit sont indépendants et suivent une distribution symétrique. Cependant, la caractérisation numérique de ces régions de confiance est loin d'être triviale. Cet article montre que l'analyse par intervalles, utilisée par ailleurs pour aborder des problèmes d'estimation à erreurs bornées, peut être employée pour caractériser les régions de confiance exactes et non-asymptotiques définies par LSCR ou SPS