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

Thakolsri, Srisakul; Kellerer, Wolfgang; Steinbach, Eckehard

doi:10.1109/chinacom.2009.5339767

Cited by 8 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step computes the number of flows to block or re-admit. This step computes only one metric, either (14) or (15). The computation complexity for this step, O (1), is independent of the number of flows and priority classes.…”

Section: Computational Complexitymentioning

confidence: 99%

“…The average system delay crosses the lower threshold at 9.25 s. The hysteresis output decreases exponentially, according to (15), with each window cycle having system delay below the lower threshold. The lower the hysteresis output, the higher the re-admission probability.…”

Section: Joint Scheduling and Priority Class Filter Performancementioning

confidence: 99%

“…The optimizer takes into account the content characteristics of the video streams and performs video rate adaptation according to the available radio resources at the eNodeB. For instance, the authors in [14,15] proposed a QoE-based video delivery by introducing two modules located inside the RAN. The two modules are the traffic engineering and traffic management module.…”

Section: Proxy-based Content-aware Resource Allocationmentioning

confidence: 99%

“…Flows are blocked or re-admitted according to the following rules: (15) where N block j * is the number of flows blocked for class j * and N re−admit j * is the number of flows re-admitted. The number of flows to block or re-admit is taken once every t w scheduling epochs.…”

Section: Step Onementioning

confidence: 99%

“…This strategy requires video processing to extract content information and/or signaling to deliver video content information at the eNodeB. The other approach, proposed in [14][15][16][17], is the utilization of a content-blind packet scheduler, such as proportional fair (PF), modified largest weighted delay first (M-LWDF), or exponential proportional fair (EXP-PF), at the eNodeB where content-aware radio resource allocation is performed at a proxy located close to the eNodeB. In this approach, a rate adaptation module avoids congestion at the eNodeB.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

QoE-driven multi-user scheduling and rate adaptation with reduced cross-layer signaling for scalable video streaming over LTE wireless systems

Khan

Martini

2016

J Wireless Com Network

View full text Add to dashboard Cite

The scarcity of the available radio spectrum coupled with the growing popularity of bandwidth intensive mobile video applications poses a huge challenge to network operators. The solution of over-provisioning the network is not economical; hence, an appropriate strategy for scheduling and resource allocation among the users in the system is of crucial importance. This work focuses on scheduling multiple video flows on the downlink of a wireless system based on orthogonal frequency division multiple access (OFDMA), such as Long-Term Evolution (LTE) and LTE-A (LTE-Advanced) standards. We propose a joint multi-user scheduling and multi-user rate adaptation strategy providing an appropriate trade-off between efficiency and fairness, while ensuring high quality of experience (QoE) for the end users. We consider Scalable Video Coding (SVC) which facilitates the truncation of bit streams, thus allowing graceful degradation of video quality in the event of wireless channel variations or network congestion. The proposed scheduler utilizes QoE-aware priority marking, where video layers are mapped to priority classes and targets at minimizing delay bound violations for the most important priority classes under congestion. In order to reduce congestion, we propose multi-user rate adaptation at the MAC layer via a novel dynamic filtering policy for QoE-based priority classes. Simulation results show that the proposed approach delivers to the end users a similar QoE as delivered by the stateof-the-art cross-layer approaches, where extensive cross-layer signaling, additional video rate adaptation modules at the core network, and frequent link probing from the wireless access network to the rate adaptation modules are required. The latter approaches are not implemented in real systems due to the aforementioned drawbacks, while our approach can be implemented without major modifications in the standard behavior of existing networks and equipment. The proposed framework can deliver delay-sensitive traffic as well as delay-tolerant best-effort traffic.

show abstract

Section: Computational Complexitymentioning

confidence: 99%

Section: Joint Scheduling and Priority Class Filter Performancementioning

confidence: 99%

Section: Proxy-based Content-aware Resource Allocationmentioning

confidence: 99%

Section: Step Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

QoE-driven multi-user scheduling and rate adaptation with reduced cross-layer signaling for scalable video streaming over LTE wireless systems

Khan

Martini

2016

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

QoE-driven resource allocation for mobile IP services in wireless network

Fei

Xing

2014

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

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

Nasralla

Khan

Martini

2018

Computer Communications

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 8 publications

References 17 publications

QoE-driven multi-user scheduling and rate adaptation with reduced cross-layer signaling for scalable video streaming over LTE wireless systems

QoE-driven multi-user scheduling and rate adaptation with reduced cross-layer signaling for scalable video streaming over LTE wireless systems

QoE-driven resource allocation for mobile IP services in wireless network

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

Contact Info

Product

Resources

About