Elastic QoS Scheduling with Step-by-Step Propagation in IEEE 802.11e Networks with Multimedia Traffic

Ruscelli, Anna Lina; Cecchetti, Gabriele; Castoldi, P.

doi:10.1155/2019/2925891

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Future studies will explore the potential for using the proposed TMA algorithm to improve the performance of various types of network, including wireless mesh networks (WMNs) [22][23][24] and vehicular ad hoc networks (VANETs) [25][26][27]. In addition, the effect of the wireless channel error on the video transmission quality should be considered into the proposed TMA algorithm for determining the AC queue mapping [2,7,15].…”

Section: Discussionmentioning

confidence: 99%

Throughput‐Based Mapping Algorithm for Video Streaming over IEEE 802.11e WLAN

Lin

Shieh

Hwang³

et al. 2021

Wireless Communications and Mobile Computing

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IEEE 802.11e utilizes the Enhanced Distributed Channel Access (EDCA) protocol to provide quality of service (QoS) support over WLANs. However, under EDCA, all video data are allocated to the same access category (AC) queue, irrespective of their coding importance. Consequently, the quality of the transmitted video may be severely degraded under heavy traffic load conditions. The literature contains several queue mapping mechanisms based on the settings of the AC queues, such as contention window size, for improving the video transmission quality over IEEE 802.11e WLANs. However, these mechanisms fail to consider the effect of the dequeuing time on the video delivery performance. Accordingly, the present study proposes a new queue mapping algorithm, designated as the throughput-based mapping algorithm (TMA), for improving the video transmission quality and reducing the video delay by dynamically allocating the video data packets to different AC queues based on their length and throughput such that the probability of the dequeuing time being reduced is increased. In implementing the proposed approach, the throughputs of the AC queues are analyzed using two Markov chain models. The simulation results show that TMA outperforms previously proposed static and dynamic queue mapping schemes in terms of a reduced AC queue length, an improved PSNR (peak signal-to-noise ratio), and a shorter transmission delay.

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Section: Discussionmentioning

confidence: 99%

Throughput‐Based Mapping Algorithm for Video Streaming over IEEE 802.11e WLAN

Lin

Shieh

Hwang³

et al. 2021

Wireless Communications and Mobile Computing

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“…coordination channel access (HCCA) [6][7][8]. The DCF protocol is a legacy channel access control protocol.…”

mentioning

confidence: 99%

“…Nevertheless, the EDCA protocol cannot guarantee strict QoS to real-time applications. Whereas by using appropriate scheduler, the HCCA protocol can provide soft QoS guarantee to real-time applications [8].…”

mentioning

confidence: 99%

Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

et al. 2022

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The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

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“…QoS evaluation and estimation based on analysing traffic factors needs techniques to process and analyse transmission of packets (Ruscelli et al, 2019), , (Alanazi and Elleithy, 2015), (Toral-Cruz et al, 2013) and (Moore and Zuev, 2005). An example of a technique that presented prospective for QoS evaluation is artificial neural network (ANN)…”

Section: Artificial Intelligence and Network Qosmentioning

confidence: 99%

Hybrid computer network quality of service and experience: development and evaluation

Salama¹

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Multimedia transmission over wired and wireless (hybrid) networks is increasingly needed as new services emerge and hybrid networks become more diverse and reliable. Quantifying quality of multimedia applications transmitted over hybrid networks is valuable for measuring network performance and its optimisation. For video, the process involves examining the images that make up the video, by quantifying distortion, noise, and complementing them with traffic parameters characterised by packet delay, delay variation (jitter) and percentage of packet loss ratio (%PLR). Processing all received packets to evaluate the quality of received application is computationally intensive. The study developed a new multi-input adaptive sampling method that allowed a subset of transmitted packets to be chosen according to variations in three synchronised traffic parameters inputs. The method integrated fuzzy logic and regression modelling of traffic parameters and adaptively adjusted the number of packets selected for processing. Statistical and neural networks methods were developed to evaluate quality of service (QoS) for video streaming and Voice over Internet Protocol (VoIP) transmitted over hybrid networks. The traffic parameters for QoS evaluations were delay, jitter and %PLR. The work involved, Bayesian classification and probabilistic neural network (PNN) based methods to process traffic parameters. QoS. This allocation conformed to the International Telecommunication Union (ITU) recommendations. Overall, the performance of Bayesian method was better than PNN when determining QoS for VoIP. In addition, the developed methods were successfully used in practical tests to analyse QoS in the wireless standards IEEE 802.11ac and IEEE 802.11n. QoS reflects provides information that indicates the extent the traffic parameters for an application are within the expected bounds. However, the user's perception of the received application is also relevant. This evaluation can be performed through quality of experience (QoE) analysis. For video, QoE considers issues such as image distortion and noise that in this study were quantified by structural similarity index measure (SSIM), peak signal to noise ratio (PSNR) and image difference (ID). A modular fuzzy logic-based system that individually determined QoS and QoE, then combined them to determine the overall quality of a wirelessly transmitted video was developed. The performance of the devised video quality evaluation system was compared against the subjective evaluation performed by 25 participants (i.e. mean opinion scores) and consistent results were observed. A further evaluation of the video quality evaluation system was carried by comparing its results against a recently reported video quality assessment method known as the spatial efficient entropic variation quality assessment. Again, comparable results were obtained between the two methods. The QoE evaluations were carried out both in a network laboratory and over an institutional network. The study resulted in development a multi-input adaptive sampling method and artificial intelligence and statistical based QoS and QoE evaluation methods. The proposed schemes improved the QoS and QoE assessments for multimedia applications. The devised adaptive sampling model in comparison with random, stratified and systematic non-adaptive sampling methods was more effective as it represented the traffic more precisely. The developed two probabilistic QoS methods showed consistency in their classifications. Both models successfully classified the received VoIP packets into their corresponding low, medium, and high QoS types. Furthermore, QoE with image partitioning approach has improved QoE evaluation as partitioned image approach provided more accurate results than full image approach. The proposed integration approach of three multimedia parameters SSIM, PSNR and ID improved accuracy of overall QoE assessments compared to single parameter approaches.

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Elastic QoS Scheduling with Step-by-Step Propagation in IEEE 802.11e Networks with Multimedia Traffic

Cited by 8 publications

References 30 publications

Throughput‐Based Mapping Algorithm for Video Streaming over IEEE 802.11e WLAN

Throughput‐Based Mapping Algorithm for Video Streaming over IEEE 802.11e WLAN

Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

Hybrid computer network quality of service and experience: development and evaluation

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