Optimized Adaptive Streaming Representations based on System Dynamics

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-control strategies at the client side, we take in this paper a providers' perspective and propose solutions to improve user satisfaction by optimizing the encoding rates of the video sequences. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network dynamics, the video content information, and the user population characteristics. The solution of the optimization is a set of encoding parameters that permit to create different streams to robustly satisfy users' requests over time. We simulate multiple adaptive streaming sessions characterized by realistic network connections models, where the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results further show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness a mong users and to reduce network resource usage. We finally propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content

User-Adaptive Editing for 360 degree Video Streaming with Deep Reinforcement Learning

International audienceThe development through streaming of 360°videos is persistently hindered by how much bandwidth they require. Adapting spatially the quality of the sphere to the user's Field of View (FoV) lowers the data rate but requires to keep the playback buffer small, to predict the user's motion or to make replacements to keep the buffered qualities up to date with the moving FoV, all three being uncertain and risky. We have previously shown that opportunistically regaining control on the FoV with active attention-driving techniques makes for additional levers to ease streaming and improve Quality of Experience (QoE). Deep neural networks have been recently shown to achieve best performance for video streaming adaptation and head motion prediction. This demo presents a step ahead in the important investigation of deep neural network approaches to obtain user-adaptive and network-adaptive 360°video streaming systems. In this demo, we show how snap-changes, an attention-driving technique, can be automatically modulated by the user's motion to improve the streaming QoE. The control of snap-changes is made with a deep neural network trained on head motion traces with the Deep Reinforcement Learning strategy A3C

ESCENA CAMPESTRE [Material gráfico]

ADQUIRIDA POR EL COLECCIONISTA EN LAS PALMAS DE GRAN CANARIAFOTO DE ESCENA CAMPESTRE EN TENERIFE, FINALES DEL SIGLO XIXCopia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

TRACK: A Multi-Modal Deep Architecture for Head Motion Prediction in 360-Degree Videos

International audienceHead motion prediction is an important problem with 360 • videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article, we introduce a new deep architecture, named TRACK, that benefits both from the history of past positions and knowledge of the video content. We show that TRACK achieves state-of-the-art performance when compared against all recent approaches considering the same datasets and wider prediction horizons: from 0 to 5 seconds

Reconfiguring Network Slices at the Best Time With Deep Reinforcement Learning

International audienceThe emerging 5G induces a great diversity of use cases, a multiplication of the number of connections, an increase in throughput as well as stronger constraints in terms of quality of service such as low latency and isolation of requests. To support these new constraints, Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies have been coupled to introduce the network slicing paradigm. Due to the high dynamicity of the demands, it is crucial to regularly reconfigure the network slices in order to maintain an efficient provisioning of the network. A major concern is to find the best frequency to carry out these reconfigurations, as there is a tradeoff between a reduced network congestion and the additional costs induced by the reconfiguration. In this paper, we tackle the problem of deciding the best moment to reconfigure by taking into account this trade-off. By coupling Deep Reinforcement Learning for decision and a Column Generation algorithm to compute the reconfiguration, we propose Deep-REC and show that choosing the best time during the day to reconfigure allows to maximize the profit of the network operator while minimizing the use of network resources and the congestion of the network. Moreover, by selecting the best moment to reconfigure, our approach allows to decrease the number of needed reconfigurations compared to an algorithm doing periodic reconfigurations during the day

Long-term follow-up of certolizumab pegol in uveitis due to immune-mediated inflammatory diseases : multicentre study of 80 patients

Objectives To evaluate effectiveness and safety of certolizumab pegol (CZP) in uveitis due to immune-mediated inflammatory diseases (IMID). Methods Multicentre study of CZP-treated patients with IMID uveitis refractory to conventional immunosuppressant. Effectiveness was assessed through the following ocular parameters: best-corrected visual acuity, anterior chamber cells, vitritis, macular thickness and retinal vasculitis. These variables were compared between the baseline, and first week, first, third, sixth months, first and second year. Results We studied 80 (33 men/47 women) patients (111 affected eyes) with a mean age of 41.6±11.7 years. The IMID included were: spondyloarthritis (n=43), Behçet's disease (n=10), psoriatic arthritis (n=8), Crohn's disease (n=4), sarcoidosis (n=2), juvenile idiopathic arthritis (n=1), reactive arthritis (n=1), rheumatoid arthritis (n=1), relapsing polychondritis (n=1), Conclusions CZP seems to be effective and safe in uveitis related to different IMID, even in patients refractory to previous biological drugs

A cost model for green fog computing and networking

International audience5G services and 4K will stress even more the future access/aggregation networks, where video contents have already become the main traffic contributor. The deployment in the convergent access of local micro data-centers (DCs) nodes is one promising approach to successfully manage this challenge. These nodes will be responsible for switching enormous amounts of traffic and simultaneously performing heavy CPU tasks (such as video or radio base band processing). Only a flexible management of these nodes based on NFV, SDN and data analytics will enable to meet these tasks. In this paper, we present a consistent and complete cost model collecting main trade-offs between energy savings and CPU processing suitable to be used in such a flexible management framework

Adaptive video streaming and fixed-mobile convergence: A good team to reduce backhaul power consumption and improve users' QoE

International audienc