Cross-Layer Optimization With Model-Based Parameter Exchange

Saul, Andreas; Khan, Shoaib; Auer, Gunther; Kellerer, Wolfgang; Steinbach, Eckehard

doi:10.1109/icc.2007.945

Cited by 16 publications

(8 citation statements)

References 13 publications

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“…The MATLAB-based LTE simulator [8] is used to simulate the mobile system and to verify feasibility of the proposed tuning algorithm. All parameters used in our simulation are given in Table I.…”

Section: Simulation Resultsmentioning

confidence: 99%

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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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Section: Simulation Resultsmentioning

confidence: 99%

“…We adopt the long term link layer model in the LTE base station as proposed by Saul et al [8], since it is less complex in terms of parameter estimation, parameter exchange, and performing the optimization. Hence, the network operator is flexible in placement of the QoE optimization module anywhere in its network.…”

Section: Radio Link Layer Modelmentioning

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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show abstract

“…Based on the long term link layer model originally proposed in [12], the long term data rate for the user k can be described as:…”

Section: Radio Link Layer Modelmentioning

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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“…To determine the uplink resources (i.e., c k in (2)) provided to each mobile terminal, we use the long-term abstraction model proposed in [15]. The model defines the actual data rate c k for user k as a function of its resource share α k and its maximum achievable rate C max,k if all the PRBs are allocated exclusively to user k, cf.…”

Section: B Uplink Resource Allocationmentioning

confidence: 99%

“…The model defines the actual data rate c k for user k as a function of its resource share α k and its maximum achievable rate C max,k if all the PRBs are allocated exclusively to user k, cf. (15).…”

Section: B Uplink Resource Allocationmentioning

confidence: 99%

QoE-driven live and on-demand LTE uplink video transmission

Essaili

Zhou

Schroeder

et al. 2011

2011 IEEE 13th International Workshop on Multimedia Signal Processing

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Abstract-We consider the joint upstreaming of live and ondemand user-generated video content over LTE using a Qualityof-Experience driven approach. We contribute to the state-of-theart work on multimedia scheduling in three aspects: 1) we jointly optimize the transmission of live and time-shifted video under scarce uplink resources by transmitting a basic quality in realtime and uploading a refined quality for on-demand consumption.2) We propose a producer-consumer deadline-aware scheduling algorithm that incorporates both the physical state of the mobile producer (e.g., cache fullness) and the scheduled playout time at the end-user. 3) We show that the scheduling decisions in 1) and 2) can be determined locally for each mobile producer. We additionally present an analytical framework for de-centralized scalable video transmission and prove that there exists an optimal solution to our problem. Simulation results for LTE uplink further demonstrate the significance of our proposed optimization on the overall user experience.

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Cross-Layer Optimization With Model-Based Parameter Exchange

Cited by 16 publications

References 13 publications

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

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

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

QoE-driven live and on-demand LTE uplink video transmission

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