Simple Optimization Algorithm for MOS-Based Resource Assignment

Saul, Andreas

doi:10.1109/vetecs.2008.404

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…However, they consider only the requested bandwidth per application and do not differentiate between different application quality levels. In , the authors focus on cross‐layer optimization between application layer and medium access control layer. The application parameters are given by a generic mathematical model for the Mean Opinion Score (MOS) as a metric for the perceived quality.…”

Section: Background and Related Workmentioning

confidence: 99%

Modelling and performance analysis of application‐aware resource management

et al. 2015

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Summary Application‐aware resource management is the approach to tailor access networks to have characteristics beneficial for the running applications and services. This is achieved through the monitoring and integration of key performance indicators from the application layer within the network resource management. The aim is to increase user‐perceived quality and network resource efficiency by traffic engineering with the help of these indicators. Using analytic and simulative approaches, this paper provides analysis methods for network operators to quantify the performance gains of alternative resource allocation algorithms that implement the application‐aware concept. Network operators can use the proposed methods to evaluate possible performance gain trade‐offs between investing in a pure capacity increase (over‐provisioning) and the realization of an application‐aware resource allocation. For this purpose, we model and analyse the application quality trade‐offs of four algorithms for application‐aware resource management at a single link in varying traffic situations. The algorithms are chosen with respect to different complexity and implementation level in order to cover the design space in a systematic way. The study of the algorithms focuses on the application‐layer performance for the most used applications today, namely, web browsing and video streaming with constant bit rate as well as hypertext transfer protocol progressive streaming with variable bit rate. Application quality trade‐offs are analysed in particular for a high‐resource utilization at a bottleneck link. The results confirm that application‐aware resource management outperforms best‐effort resource management in terms of quality of experience. Moreover, our study provides guidelines for the selection and configuration of the evaluated algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Background and Related Workmentioning

confidence: 99%

Modelling and performance analysis of application‐aware resource management

et al. 2015

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“…In , QoE metrics are used to differentiate between different services and thus, generate a one‐dimensional optimisation function for the radio resource management. This approach can be seen as a continuation of former work which used a so‐called utility function for resource management to define a scheduling order with respect to different application cases .…”

Section: Related Workmentioning

confidence: 99%

Using buffered playtime for QoE‐oriented resource management of YouTube video streaming

Wamser

Hock

Seufert

et al. 2013

Trans. Emerging Tel. Tech.

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YouTube is the most important online platform for streaming video clips. The popularity and the continuously increasing number of users pose new challenges for Internet service providers. In particular, in access networks where the transmission resources are limited and the providers are interested in reducing their operational expenditure, it is worth to efficiently optimise the network for popular services such as YouTube. In this paper, we propose different resource management mechanisms to improve the quality of experience (QoE) of YouTube users. In particular, we investigate the benefit of cross-layer resource management actions at the client and in the access network for YouTube video streaming. The proposed algorithms are evaluated in a wireless mesh testbed. The results show how to improve the YouTube QoE for the users with the help of client-based or network-based control actions

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“…Despite the popularity of QoS-oriented schedulers design, the end-users -humans -have the decisive judgment about the received service quality. In literature, some pioneering authors (Khan et al, 2007;Saul, 2008;Saul and Auer, 2009;Thakolsri et al, 2009) showed that the application of a subjective-based approach may lead influence the QoE in multimedia services over communication systems are presented, along with some QoE estimation methods; Section 3 illustrates some scheduling algorithms, ranging from the simplest ones to QoS-aware approaches, followed by the introduction of QoS-QoE mapping strategies and utility-based optimization. Section 4 provides the main contribution of this survey, namely the presentation of recent research directions regarding QoEoriented wireless resources scheduling -state-of-the-art QoE-aware scheduling methods are discussed and classified based on the adjustments required, at the end-user devices, in order to implement the different scheduling strategies on wireless systems.…”

Section: Introductionmentioning

confidence: 99%

A survey on QoE-oriented wireless resources scheduling

Sousa

Queluz

Rodrigues

2020

Journal of Network and Computer Applications

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Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.

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Simple Optimization Algorithm for MOS-Based Resource Assignment

Cited by 8 publications

References 6 publications

Modelling and performance analysis of application‐aware resource management

Modelling and performance analysis of application‐aware resource management

Using buffered playtime for QoE‐oriented resource management of YouTube video streaming

A survey on QoE-oriented wireless resources scheduling

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