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UNDERSTANDING THE PERFORMANCE OF INTERNET VIDEO OVER RESIDENTIAL NETWORKS
, 2012
"... Video streaming applications are now commonplace among home Internet users, who typically access the Internet using DSL or Cable technologies. However, the effect of these technologies on video performance, in terms of degradations in video quality, is not well understood. To enable continued deploy ..."
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Video streaming applications are now commonplace among home Internet users, who typically access the Internet using DSL or Cable technologies. However, the effect of these technologies on video performance, in terms of degradations in video quality, is not well understood. To enable continued deployment of applications with improved quality of experience for home users, it is essential to understand the nature of network impairments and develop means to overcome them. In this dissertation, I demonstrate the type of network conditions experienced by Internet video traffic, by presenting a new dataset of the packet level performance of real-time streaming to residential Internet users. Then, I use these packet level traces to evaluate the performance of commonly used models for packet loss simulation, and finding the models to be insufficient, present a new type of model that more accurately captures the loss behaviour. Finally, to demonstrate how a better understanding of the network can improve video quality in a real application scenario, I evaluate the performance of forward error correction schemes for Internet video using the measurements. I show that performance can be poor, devise a new metric to predict performance of error recovery from the characteristics of the input, and
A.: Machine Learning Approach for Quality of Experience Aware Networks
- In: IEEE INCoS (2010
"... Abstract-Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This pr ..."
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Abstract-Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This process usually incorporates complex subjective studies that need to recreate the viewing conditions of the service in a controlled environment. In this paper we present Machine Learning techniques for modeling the dependencies of different network and application layer quality of service parameters to the QoE of network services using subjective quality feedback. These accurate QoE prediction models allow us to further develop a geometrical method for calculating the possible remedies per network stream for reaching the desired level of QoE. Finally we present a set of possible network techniques that can deliver the desired improvement to the multimedia streams.
Temporally Coherent Adaptive Sampling for Imperfect Shadow Maps
"... We propose a new adaptive algorithm for determining virtual point lights (VPL) in the scope of real-time instant radiosity methods, which use a limited number of VPLs. The proposed method is based on Metropolis-Hastings sampling and exhibits better temporal coherence of VPLs, which is particularly i ..."
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We propose a new adaptive algorithm for determining virtual point lights (VPL) in the scope of real-time instant radiosity methods, which use a limited number of VPLs. The proposed method is based on Metropolis-Hastings sampling and exhibits better temporal coherence of VPLs, which is particularly important for real-time appli-cations dealing with dynamic scenes. We evaluate the properties of the proposed method in the context of the algorithm based on imperfect shadow maps and compare it with the commonly used inverse transform method. The results indicate that the proposed technique can significantly reduce the temporal flickering artifacts even for scenes with complex materials and textures. Further, we propose a novel splatting scheme for imperfect shadow maps using hardware tessellation. This scheme significantly improves the rendering performance particularly for complex and deformable scenes. We thoroughly analyze the performance of the proposed techniques on test scenes with detailed materials, moving camera, and deforming geometry.
A quality of experience management module
, 2011
"... Abstract-User centric management of multimedia services necessitates understanding of how different system parameters affect the perceived quality of the service. Today's multimedia delivery systems are highly complex and diverse and span over multiple domains commonly in control of different ..."
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Abstract-User centric management of multimedia services necessitates understanding of how different system parameters affect the perceived quality of the service. Today's multimedia delivery systems are highly complex and diverse and span over multiple domains commonly in control of different entities. Furthermore, the end user devices are highly variable in capabilities and characteristics, which directly affect the perceived quality. In addition to these factors the perceived quality in multimedia is subjective by nature, which leads to complexities in accurate estimation with objective methods and costs with subjective estimation. To deal with the necessities of estimation of Quality of Experience (QoE) we have implemented a software module that aggregates information from probes and subjective quality feedback to develop QoE prediction models. These models are then used as part of the QoE management module to estimate the QoE of multimedia delivery streams in real-time. Additionally, the module calculates and suggests QoE remedies (improvements) for multimedia streams that underperform. The remedies are in the form of a single or multiple target parameter values that need to be reached in order for the stream to reach the desired QoE. The functionalities of this module enable an implementation of a user-centric management loop for multimedia services.
Managing Quality of Experience on a Commercial Mobile TV Platform
"... Abstract – The user perceived quality or Quality of Experience (QoE) is of significant importance to multimedia service providers because of its relevance for efficient management of provided services. However, due to its subjective nature, QoE is difficult to estimate. Subjective methods are costly ..."
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Abstract – The user perceived quality or Quality of Experience (QoE) is of significant importance to multimedia service providers because of its relevance for efficient management of provided services. However, due to its subjective nature, QoE is difficult to estimate. Subjective methods are costly and impractical, while objective methods do not correlate precisely with the subjective perception. In addition to the challenges in estimating QoE, further challenges are presented in determining the means of managing the QoE in today’s complex and varied multimedia distribution systems. As a result of the high number of components and parameters that affect the perceived quality, from content creation to delivery and presentation, the QoE aware management in these highly versatile environments becomes increasingly difficult. We present a method that uses limited initial subjective tests to develop prediction models for QoE as perceived by the viewers. This minimizes the complexities associated with subjective methods while maintaining the accuracy. Further we present a method of calculating the QoE remedies for managing the QoE per stream, based on the QoE prediction models.
Estimations and Remedies for Quality of Experience in Multimedia Streaming
"... Abstract — Managing multimedia network services in a User-centric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality o ..."
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Abstract — Managing multimedia network services in a User-centric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality of Experience (QoE) is such a metric, which captures many different aspects that compose the perception of quality. The drawback of using QoE is that due to its subjectiveness, accurate measurement necessitates execution of cumbersome subjective studies. In this work we propose a method that uses Machine Learning techniques to build QoE prediction models based on limited subjective data. Using those models we have developed an algorithm that generates the remedies for improving the QoE of observed multimedia stream. Selecting the optimal remedy is done by comparing the costs in resources associated to each of them. Coupling the QoE estimation and calculation of remedies produces a tool for effective implementation of a User-centric management loop for multimedia streaming services.
