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Proceedings ArticleDOI

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

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TLDR
The second order statistics of the number of packet losses in finite Markov models over several relevant time scales are derived and adapted to loss processes visible in wired and wireless transmission channels.
Abstract
Real-time Internet services are gaining in popularity due to rapid provisioning of broadband access technologies. Delivery of high quality of experience (QoE) is important for consumer acceptance of multimedia applications. IP packet level errors affect QoE and the resulting quality degradations have to be taken into account in network operation. We derive the second order statistics of the number of packet losses in finite Markov models over several relevant time scales and adapt them to loss processes visible in wired and wireless transmission channels. Higher order Markov chains offer a large set of parameters to be exploited by complex fitting procedures. We experience that the 2-state Gilbert-Elliott model already captures a wide range of observed loss pattern appropriately and discuss how such models can be used to examine the quality degradations caused by packet losses.

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Journal ArticleDOI

Optimal Streaming Codes for Channels With Burst and Arbitrary Erasures

TL;DR: Numerical results demonstrate that the optimal streaming codes outperform existing streaming codes of comparable complexity over some instances of the Gilbert–Elliott channel and the Fritchman channel.
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Optimal Streaming Codes for Channels with Burst and Arbitrary Erasures

TL;DR: Numerical results demonstrate that the optimal streaming codes outperform existing streaming codes of comparable complexity over some instances of the Gilbert-Elliott channel.
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Evaluating the adaptivity of computing systems

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References
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Journal ArticleDOI

A tutorial on hidden Markov models and selected applications in speech recognition

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI

On the self-similar nature of Ethernet traffic (extended version)

TL;DR: It is demonstrated that Ethernet LAN traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal-like behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks.
Journal ArticleDOI

Self-similarity in World Wide Web traffic: evidence and possible causes

TL;DR: It is shown that the self-similarity in WWW traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in file transfer, the effect of user "think time", and the superimposition of many such transfers in a local-area network.
Journal ArticleDOI

Capacity of a burst-noise channel

TL;DR: The capacity C of the model channel exceeds the capacity C(sym. bin.) of a memoryless symmetric binary channel with the same error probability; however, the difference is slight for some values of h, p, P; then, time-division encoding schemes may be fairly efficient.
Journal ArticleDOI

Self-similarity in World Wide Web traffic: evidence and possible causes

TL;DR: It is shown that the self-similarity in WWW traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in file transfer, the effect of user "think time", and the superimposition of many such transfers in a local area network.