731 research outputs found
QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning
Due to the fluctuation of throughput under various network conditions, how to
choose a proper bitrate adaptively for real-time video streaming has become an
upcoming and interesting issue. Recent work focuses on providing high video
bitrates instead of video qualities. Nevertheless, we notice that there exists
a trade-off between sending bitrate and video quality, which motivates us to
focus on how to get a balance between them. In this paper, we propose QARC
(video Quality Awareness Rate Control), a rate control algorithm that aims to
have a higher perceptual video quality with possibly lower sending rate and
transmission latency. Starting from scratch, QARC uses deep reinforcement
learning(DRL) algorithm to train a neural network to select future bitrates
based on previously observed network status and past video frames, and we
design a neural network to predict future perceptual video quality as a vector
for taking the place of the raw picture in the DRL's inputs. We evaluate QARC
over a trace-driven emulation. As excepted, QARC betters existing approaches.Comment: Accepted by ACM Multimedia 201
Understanding user experience of mobile video: Framework, measurement, and optimization
Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study
Intelligent Video Ingestion for Real-time Traffic Monitoring
This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordAs an indispensable part of modern critical infrastructures, cameras deployed at strategic places and prime junctions in an intelligent transportation system (ITS), can help operators in observing traffic flow, identifying any emergency situation, or making decisions regarding road congestion without arriving on the scene. However, these cameras are usually equipped with heterogeneous and turbulent networks, making the realtime smooth playback of traffic monitoring videos with high quality a grand challenge. In this paper, we propose a light-weight Deep Reinforcement Learning (DRL) based approach, namely sRC-C (smart bitRate Control with a Continuous action space), to enhance the quality of realtime traffic monitoring by adjusting the video bitrate adaptively. Distinguished from the existing bitrate adjusting approaches, sRC-C can overcome the bias incurred by deterministic discretization of candidate bitrates by adjusting the video bitrate with more f ine-grained control from a continuous action space, thus significantly improving the Quality-of-Service (QoS). With carefully designed state space and neural network model, sRC-C can be implemented on cameras with scarce resources to support real-time live video streaming with low inference time. Extensive experiments show that sRC-C can reduce the frame loss counts and hold time by 24% and 15.5%, respectively, even with comparable bandwidth utilization. Meanwhile, compared to the-state-of-art approaches, sRC-C can improve the QoS by 30.4%.National Key Research and Development Program of ChinaEuropean Union Horizon 2020Leading Technology of Jiangsu Basic Research PlanNational Natural Science Foundation of ChinaChongqing Key Laboratory of Digital Cinema Art Theory and Technolog
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding
strategies without any presumptions, has become one of the research hotspots
for adaptive streaming. However, it typically suffers from several issues,
i.e., low sample efficiency and lack of awareness of the video quality
information. In this paper, we propose Comyco, a video quality-aware ABR
approach that enormously improves the learning-based methods by tackling the
above issues. Comyco trains the policy via imitating expert trajectories given
by the instant solver, which can not only avoid redundant exploration but also
make better use of the collected samples. Meanwhile, Comyco attempts to pick
the chunk with higher perceptual video qualities rather than video bitrates. To
achieve this, we construct Comyco's neural network architecture, video datasets
and QoE metrics with video quality features. Using trace-driven and real-world
experiments, we demonstrate significant improvements of Comyco's sample
efficiency in comparison to prior work, with 1700x improvements in terms of the
number of samples required and 16x improvements on training time required.
Moreover, results illustrate that Comyco outperforms previously proposed
methods, with the improvements on average QoE of 7.5% - 16.79%. Especially,
Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average
video quality under the same rebuffering time.Comment: ACM Multimedia 201
Wireless Channel Model and LDM-Based Transmission with Unequal Error Protection for Inside Train Communications
Although the deployment of wireless systems is widespread, there are still sectors where they are not used due to their lack of reliability in comparison to wired systems. Sectors like industry or vehicle communications consider their environment hostile because the wireless signals suffer a lot of interferences. One of such environments is the railway sector, where wiring removal will allow more flexibility for both control and monitoring systems. This thesis analyzes wireless communications inside train cars, aiming at modelling their behavior and at proposing techniques to increase the reliability of the critical signals among train systems, wich can coexist with other lower priority systems. After proposing a novel model of an inside train wireless channel, a transmission system based on Layered Division Multiplexing (LDM) has been proposed which theoretically promises higher capacities than traditional TDM or FDM. This capacity gain is used to provide higher reliability to critical data using Unequal Error Protection (UEP) while maintaining the same bit rate as equivalent TDM or FDM based systems. In the final part of the thesis, simulation results of the proposed LDM system are provided, combined with Alamouti space time coding and different coding rates. Multiantenna extensions of the proposed LDM schemes are also simulated, providing BER and throughput results. These results will be used to shed light about how to reduce BER of an inside train wireless communication system.Aunque el despliegue de los sistemas inalámbricos está muy extendido, aun hay sectores donde no se utiliza por la poca fiabilidad que proporcionan comparado con los sistemas cableados. Sectores como la industria o las comunicaciones vehiculares consideran el entorno donde trabajan como entorno hostil, debido a que las señales inalámbricas sufren muchas interferencias. Uno de estos entornos es el de las comunicaciones en ferrocarril donde la eliminación de cables permitirÃa mayor flexibilidad entre los sistemas de control y monitorización. En esta tesis se analiza el canal de comunicación inalámbrico dentro de los trenes, con el objetivo de modelar su comportamiento y proponer técnicas que permitan aumentar la fiabilidad de la información de tipo crÃtico transmitida entre los sistemas del tren, repercutiendo lo menos posible en otros sistemas de menor prioridad. Tras proponer el modelo de canal inalámbrico dentro del tren, se ha propuesto un sistema de transmisión basado en Layered Division Multiplexing (LDM) que analizándolo teóricamente promete mayores capacidades que los tradicionales TDM o FDM. Esta capacidad se utilizará para obtener mayor redundancia de los datos crÃticos usando Unequal Error Protection (UEP) manteniendo la misma tasa de transferencia bits que los sistemas basados en TDM/FDM. En la parte final de la tesis, se obtienen resultados de las simulaciones realizadas con el sistema LDM propuesto, combinada con codificación espacio temporal como Alamouti y diferentes ratios de codificación. También se han simulado configuraciones multiantena obteniendo resultados de BER y throughput. Estos resultados servirán para arrojar luz sobre cómo reducir el BER en las comunicaciones inalámbricas dentro de los trenes.Haririk gabeko sistemak oso hedatuak dauden arren oraindik erabiltzen ez dituen sektoreak badaude ematen duten fidagarritasuna txikia delako kableatutako sistemekin alderatuz. Industria bezalako sektoreek edo ibilgailuetako komunikazioek lan egiten duten ingurua oso zaratatsua izaten da eta seinaleek interferentzia asko jasaten dituzte. Tesi honetan tren barruko haririk gabeko komunikazio kanala aztertzen da, bere portaera aztertu eta modelatzeko asmotan. Jakintza honekin zein teknika izan daitekeen erabilgarriak aztertuko da datuen fidagarritasuna handitzeko helburuarekin, lehentasun gutxiago duten sistemetan eragin txikiena izanik. Modeloa atera ondoren proposatu den transmisio sistema Layered Division Multiplexing (LDM) izan da, non azterketa teorikoek TDM edo FDM sistemek baino kapazitate gehiago dutela frogatzen dute. Kapazitate hau sistemaren datu kritikoei erredundantzia gehiago emateko erabiliko da Unequal Error Protection (UEP) erabiliz, TDM/FDM sistemetan bidaltzen den bit tasa kopurua mantenduz. Tesiaren azken partean, proposatutako LDM sistemaren simulazio emaitzak ematen dira, Alamouti espazio denbora kodifikazioarekin konbinatuak eta kodigo ratio desberdinekin. Antena anitzezko konfigurazioak ere simulatu dira BER eta throughput emaitzak lortuz. Emaitza hauek haririk gabeko tren barruko komunikazioetan BER-a nola gutxitu daitekeen jakiten lagunduko digute
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