Reputation-based network selection solution for improved video delivery quality in heterogeneous wireless network environments

Bi, Ting; Trestian, Ramona; Muntean, Gabriel‐Miro

doi:10.1109/bmsb.2013.6621734

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…In [3], Ting Bi et al as considered Heterogeneous Network such as WIFI, WIMAX and UMTS. He has evaluated QoS differently for WIFI, WIMAX and UMTS with respect to bandwidth, data rate, coverage, mobility support and quality of service parameters.…”

Section: B Classification Of Aqmmentioning

confidence: 99%

Fuzzy Cautious Adaptive Random Early Detection for Heterogeneous Network

Ravindra¹,

Patil²

2015

2015 Fifth International Conference on Advances in Computing and Communications (ICACC)

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Heterogeneous Network consists of wired and wireless network. In these networks, congestion control and quality of service is major issue. Due to this issues, delay and jitter are increased which decreases the performance of network. For reducing this issues there are various queue management techniques which are classified as Active Queue Management and Passive Queue Management. AQM consists of access routers which are used to detect congestion. Working principle of AQM is very effective for congestion control.AQM such as Random Early Detection (RED), has various types such as Adaptive-RED (ARED), Refined-ARED (RE-ARED), Combination of ARED, RE-ARED is Cautious Adaptive RED (CARED).In this paper, CARED is designed for Heterogeneous network. CARED has dynamically changing maximum drop probability (max p ) depends upon level of traffic load. FUZZY-CARED is designed to improve CARED in which max p cautiously increases and decreases after detecting congestion in the network. The proposed work aims to keep average queue length (newavg) around required target range by cautiously selecting increase max p on current traffic load. The simulation results shows comparison of ARED, CARED and FUZZY-CARED in which FUZZY-CARED gives better results than ARED, CARED.

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Section: B Classification Of Aqmmentioning

confidence: 99%

Fuzzy Cautious Adaptive Random Early Detection for Heterogeneous Network

Ravindra¹,

Patil²

2015

2015 Fifth International Conference on Advances in Computing and Communications (ICACC)

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“…However, the framework itself does not provide any network selection algorithm. To this end, various researchers build their solution on top of the IEEE 802.21 MIH architecture [4] [6][7]. A number of researchers in [6][7] have enhanced the MIH Information Server to accelerate vertical handover procedures to support the network selection and seamless handover.…”

Section: Related Workmentioning

confidence: 99%

“…User entities at higher layers employ the MIH-SAP to control or to monitor the link-layer entity and the MIHF uses the MIH-LINK-SAP as an interface together with the link layer to translate the information received from the MIH-SAP. The remote MIHF entities use the MIH-NET-SAP to exchange the information with the MIHF [3].…”

Section: System Arichitecturementioning

confidence: 99%

“…In this context, the "Always Best Connected" (ABC) vision emphasizes the scenario of a variety of RATs that work together in order to provide a global wireless infrastructure in which the end users will benefit from an optimal service delivery via the most suitable wireless network that satisfies their interests [3] among all the available wireless networks. An example of such heterogeneous wireless environment is illustrated in Fig 1, where a mobile user is roaming through a heterogeneous wireless environment while on the move from home to office.…”

Section: Introductionmentioning

confidence: 99%

“…An example of such heterogeneous wireless environment is illustrated in Fig 1, where a mobile user is roaming through a heterogeneous wireless environment while on the move from home to office. In this context, there is an increasing desire to enable the ABC paradigm within today's heterogeneous wireless network environment [4].…”

Section: Introductionmentioning

confidence: 99%

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RLoad: Reputation-based load-balancing network selection strategy for heterogeneous wireless environments

Trestian

Muntean

2013

2013 21st IEEE International Conference on Network Protocols (ICNP)

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In the current environment, network operators are trying to cope with the significant increase in data traffic by adopting different solutions to expand their network capacity. One solution would be the convergence of next generation wireless networks (e.g., HSDPA, LTE and WiMAX) which involve the closely interworking of the existing 2G/2.5G/3G networks with the new next generation networks in terms of handover and network selection. However, as a result, the diversification in mobile devices and the heterogeneity of the wireless environment make the seamless always best connectivity of the mobile users a challenge for the service providers. To this extent, we propose RLoad a novel Reputation-based Load-balancing Network Selection Strategy for heterogeneous wireless environments built on top of the IEEE 802.21 Media Independent Handover (MIH) standard. The proposed solution makes use of a reputation-based mechanism to select the most appropriate set of networks for the mobile user and a load balancing mechanism to distribute the traffic load among the networks by making use of the Multipath TCP (MPTCP) protocol. Preliminary simulation results show the significant benefits of the proposed RLoad solution.

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Solutions for Improving Rich Media Streaming Quality in Heterogeneous Network Environments

Trestian

Muntean

2022

Wiley Encyclopedia of Electrical and Electronics Engineering

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Growing amounts of data are exchanged globally, especially with the latest increase in the number and types of smart devices, from smartphones to smart TVs, which enable both generation and consumption of rich media content. In a global context, in which the internet traffic is expected to exceed several zettabytes per year and the video traffic is estimated to exceed 82% of the total data traffic by 2022, respectively, there is a significant pressure on the underlying network infrastructure to support high quality of these rich media services. The improved wired and guided network infrastructure and emerging 5G networks, and evolving LTE and Wi‐Fi networks, are providing increasing bandwidth support and improved Quality of Service for these applications. However, the pressure the rich media content, including omnidirectional, 4K, 3D, HD video, and VR/AR applications, place on these networks is also increasing with a high rate, mostly due to the number and range of services as well as the increasing number of users. As a consequence, the increased latency and loss affects the users' Quality of Experience when consuming these rich media services. However, a simple increase in system capacity will not be enough, and alternative solutions are required to fix the provisioning of QoE for rich media streaming applications. This article describes such solutions, including adaptive multimedia and multisensorial media delivery schemes, which were proposed to improve the overall user‐perceived quality, energy efficiency, and/or network utilization.

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Reputation-based network selection solution for improved video delivery quality in heterogeneous wireless network environments

Cited by 17 publications

References 13 publications

Fuzzy Cautious Adaptive Random Early Detection for Heterogeneous Network

Fuzzy Cautious Adaptive Random Early Detection for Heterogeneous Network

RLoad: Reputation-based load-balancing network selection strategy for heterogeneous wireless environments

Solutions for Improving Rich Media Streaming Quality in Heterogeneous Network Environments

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