Media Streams Planning with Transcoding

Troubil, Pavel; Rudová, Hana; Holub, Petr

doi:10.1109/nca.2013.46

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…9: for each logical link l ∈ L L do 10: loadLimit l = min h∈physical(l) (loadLimit h ) 11: for each application node v ∈ V A do 12: calculate load v,res of each resource by all streams 13: nodeOverload v = max(max res∈Res (load v,res ), 1) Calculation of loads and limits on physical links (Ln. 4) requires O( L H · S ) time.…”

Section: B Main Algorithm Loopmentioning

confidence: 99%

“…Average results from the 600 runs are shown in Table I solution, and it was always found within one second. In our previous work [11], we could solve the problem for 11 sites in 1:n scheme and 7 sites in m:n scheme, but we needed 5 seconds to do so.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…On these instances solved by the MIP solver [11], we compared values of objective function found by our algorithm to the optimal solutions. Since the MIP solver did not consider uncertainty at all, it solved simplified instance of the problem, where we set exact link capacities equal to expected (average) values of the cumulative distribution function.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In our previous work [11], we used methods of mixed integer programming (MIP) to find optimal solution of the problem without considering uncertainty at all. Because MIP solvers and other optimal methods do not have sufficient performance for the more complex problem with uncertainties, we built the new algorithm presented in this paper upon ant colony optimization (ACO) metaheuristic [12].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…UltraGrid with CoUniverse has been successfully used in many activities spanning multiple continents; e. g., a demonstration of CoUniverse with UltraGrid at SuperComputing 2013 included USA, Japan, and Czech Republic, employing low-latency HD video based on GPUaccelerated MJPEG. The intercontinental data distribution trees were planned by our previous algorithm [11].…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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Interactive collaborative environments built upon high quality video applications require minimum capture-todisplay latency to maintain feeling of natural communication. In practice, end users often encounter interruptions due to network congestion, which they could not avoid without knowledge of physical network infrastructure. We solve the problem of application level multipoint routing without assuming accurate knowledge of network link capacities, but rather relying on their estimates by network inference and bandwidth measurement tools. Our approach can handle uncertainties caused by both unreliable bandwidth measurements and unexpected capacity sharing. Support for on-the-fly transcoding of data streams on multicast agents allows per-recipient adaptation of video quality and bandwidth. We have implemented an ant colony optimization algorithm in the CoUniverse middleware for orchestration of collaborative environments. Experiments show very good quality of results on most practical use cases and ability to solve significantly larger instances than optimal approaches based on mixed integer programming.

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Section: B Main Algorithm Loopmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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Dynamic reconfiguration in multigroup multicast routing under uncertainty

2017

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Video conference based on enterprise desktop grid

Sorokin¹,

Rougier

2016

2016 23rd International Conference on Telecommunications (ICT)

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Contexte et Motivation Conférence vidéo est un sujet de télécommunications bien connu, qu'on étudiait pour les décades. Récemment ce sujet a reçu une nouvelle pulsion grâce à la bande passante accrue de réseau local et réseau étendu, et l'apparition de l'équipement vidéo de bon marché. Au même temps le vidéo de bonne qualité, comme "Full HD", peut demander les ressources computationnelles significatives pour son traitement. Le traitement vidéo pour les conférences comprend quelques manipulations nécessaires pour obtenir une expérience utilisateur avancée (mélange de plusieurs flux vidéo ou de passer l'image au participant qui parle actuellement), ainsi que les opérations causées par l'incompatibilité des paramètres, par exemple transcodage dans le cas où les participants utilisent différents codecs vidéo. Actuellement, deux architectures distinctes pour le traitement de ces tâches de manipulation de vidéo sont utilisés. La solution traditionnelle utilise Multipoint Control Unit (MCU) [1]. MCU est un composant puissant qui centralise toutes les opérations de traitement vidéo et distribue les flux résultant. MCU peut être mis en oeuvre comme une unité matérielle intégrée avec Digital Signal Processors (DSP) ou un composant logiciel installé sur les serveurs type Commercial Off-The-Shelf (COTS). Aussi MCU peut être déployé dans le nuage où au mode local. Dans tous les scénarios de déploiement, MCU représente une ressource dédiée, qui doit être acheté ou loué.

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Media Streams Planning with Transcoding

Cited by 5 publications

References 28 publications

Media Streams Planning with Uncertain Link Capacities

Media Streams Planning with Uncertain Link Capacities

Dynamic reconfiguration in multigroup multicast routing under uncertainty

Video conference based on enterprise desktop grid

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