Wireless resource allocation with perceived quality fairness

Saul, Andreas

doi:10.1109/acssc.2008.5074683

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…ii) Proxy driven radio resource allocation approach utilizes a simple packet scheduler at the RAN and reduces the congestion by performing video optimization at the crosslayer module either at the video server or at the core network. In this approach, video optimization through cross-layer rate adaptation module at the core network avoids overloading the scheduler at the RAN, as proposed in [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89]. iii) Client driven approach is a video client driven approach, utilizing for instance Dynamic Adaptive Streaming over HTTP (DASH), as proposed in [90][91][92][93][94][95][96][97].…”

Section: Taxonomymentioning

confidence: 99%

“…The authors propose MOS as the optimization metric for each of the traffic types and design a utility metric which quantifies the users' satisfaction in terms of the MOS. In [76] the authors further extend the work by introducing different objective functions such as the modified max-min MOS where the objective function guarantees a minimum MOS to each of the flows. The cross-layer optimization technique by considering the MOS of each application shows a remarkable improvement in terms of the number of satisfied users as compared to the throughput-based resource allocation schemes.…”

Section: B Proxy Driven Radio Resource Allocationmentioning

confidence: 99%

“…The cross-layer optimization technique by considering the MOS of each application shows a remarkable improvement in terms of the number of satisfied users as compared to the throughput-based resource allocation schemes. The crosslayer optimization technique considered in [75], [76] assumes that the video server is located close to the base station which allows quick rate adaptation of the video application. The rate adaptation is assumed to be performed by changing the quantization parameters of the video streams.…”

Section: B Proxy Driven Radio Resource Allocationmentioning

confidence: 99%

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Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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We present in this paper a comprehensive review and comparison of recent downlink scheduling approaches for video streaming traffic over the Orthogonal Frequency Division Multiple Access (OFDMA) based Long-Term Evolution (LTE) wireless technology. Focusing on content-aware downlink scheduling approaches, we provide an extensive literature review, a taxonomy for content-aware and content-unaware downlink schedulers, and tables that summarize the key approaches and common parameters among the schedulers. In addition, we analyze and compare via simulation the performance of some of the most relevant scheduling rules. Our main goal is to compare and analyze different classes of scheduling strategies in terms of network centric performance metrics as well as user centric metrics. Quality of Service (QoS) evaluation involves the evaluation of network performance parameters, e.g., packet loss rate, average system throughput and end-to-end packet delay. On the other hand, Quality of Experience (QoE) reflects the user's experience and satisfaction in terms of Mean Opinion Score (MOS). According to simulation results, proxy based QoE aware scheduling strategies perform best in terms of number of satisfied users and should be used in an LTE downlink to offer high quality video streaming services.

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Section: Taxonomymentioning

confidence: 99%

Section: B Proxy Driven Radio Resource Allocationmentioning

confidence: 99%

Section: B Proxy Driven Radio Resource Allocationmentioning

confidence: 99%

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Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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“…For instance, when a single user runs a very demanding application or has a very poor channel quality, the optimizer tries to give this user more resources and therefore forces all other users to share this poor experience. A modified maxmin technique [22] has been proposed to allow for setting a minimum guarantee of service quality. It first checks whether there is enough resources to provide all users with that guaranteed quality.…”

Section: A Utility-based Optimizationmentioning

confidence: 99%

Application-driven cross layer optimization for wireless networks using MOS-based utility functions

Thakolsri

Kellerer

Steinbach

2009

2009 Fourth International Conference on Communications and Networking in China

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This paper discusses a Quality of Experience (QoE) driven cross-layer optimization framework for efficient network resource allocation in wireless networks. The proposed scheme jointly optimizes the application layer and the lower layers of the wireless protocol stack with the aim of improving the user's QoE. The Mean Opinion Score (MOS) is used as a common metric for user-perceived quality in the optimization scheme. Three different QoE-based optimization schemes are compared to a throughput maximization scheme and a non-optimized system. We perform simulations using a software implementation of a developed HSDPA system. Results show that the MOS-based approaches lead to significant improvements of user perceived quality.

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“…In [5], Mean Opinion Score (MOS) was used as a common utility metric for userperceived quality in the cross-layer optimization. The proposed MOS-based scheme was extended by targeting at different objective function such as a modified max-min fairness that guarantees a minimum service quality to all users [6]. In [7], it has been shown that applying any of the MOS-based approaches leads to significant improvements of user perceived quality compared to other approaches including the throughput maximization.…”

Section: Introductionmentioning

confidence: 99%

QoE-driven cross-layer optimization in wireless networks addressing system efficiency and utility fairness

Thakolsri

Cokbulan

Jurca

et al. 2011

2011 IEEE GLOBECOM Workshops (GC Wkshps)

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We address a multi-criteria Quality of Experience (QoE) driven optimization problem for multi-user wireless video delivery. The optimization is done in a cross-layer fashion by using parameterized models of application and link layer in order to determine the application data rate and the network resources that fulfill the criteria. Mean Opinion Score (MOS) is used as a unified utility metric that encompasses the userperceived quality under certain receiving conditions for the user application. We consider two partially contrary objectives, which are maximizing the total perceived quality of the whole system and maximizing the utility fairness among all users. A tuning mechanism is proposed, which allows the network operator to change dynamically its operating point of the resource allocation based on its pre-defined policy of any combinations of these two objectives. We implemented the proposed tuning algorithm in an emulated Long-Term Evolution (LTE) system and verified its feasibility through simulations.

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Wireless resource allocation with perceived quality fairness

Cited by 12 publications

References 6 publications

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Application-driven cross layer optimization for wireless networks using MOS-based utility functions

QoE-driven cross-layer optimization in wireless networks addressing system efficiency and utility fairness

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