Cross-layer design of cognitive radio network for real time video streaming transmission

Qin, Hang; Cui, Yanrong

doi:10.1109/cccm.2009.5267918

Cited by 6 publications

(11 citation statements)

References 9 publications

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“…In the following, we survey and classify the feasible and already proved resource allocation techniques applicable for multimedia transmission over CRNs in order to guarantee QoS/ QoE as listed in Table IX. B.2.1) Machine Learning-based Resource Allocations Techniques Machine learning is supposed to provide a mechanism to guide the system reconfiguration by knowing the environment perception results and device reconfigurability in order to maximize the utility of the available resources [260]. SUs are aware of their environment in nature, but in order to be Bayesian Model [146], [224], [225] Clustering Algorithm [77], [79] Genetic Algorithm [110], [128], [130], [226] Decision Tree [108] Markov Model [29], [42], [101], [121], [139], [219], [227]- [234] Multi-agent Learning [210], [212], [235], [236] Simulated Annealing [222] Game Theory…”

Section: B2)mentioning

confidence: 99%

“…A multi-agent learning model is applicable to CRNs, where SUs are the agents and the ultimate goal of the competition is to occupy the best available primary channel. This model has been studied by [210], [212], [235], [236] for multimedia transmission over CRNs.…”

Section: B2)mentioning

confidence: 99%

“…In [236], a multi-user resource management approach was proposed called the real-time decentralized multi-agent learning algorithm, which dynamically utilizes accessible channels while using available interference information to obtain the learning efficiency. The system diagram of the proposed scheme is presented in Fig.…”

Section: B2)mentioning

confidence: 99%

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Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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Section: B2)mentioning

confidence: 99%

Section: B2)mentioning

confidence: 99%

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Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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“…Larcher et al [19] propose a paradigm in which competing SUs dynamically exchange spectral resources and adapt their transmission strategies to improve the quality of delay-sensitive multimedia applications. Hang Qin and Yanrong Cui [20] propose a distributed resource management scheme, the RDMAL (Real time Distributed Multi-Agent Learning) algorithm, for use in cognitive radio networks. RDMAL uses the multi-agent learning approach to effectively exploit available frequency channels, and uses available interference information to achieve learning efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Yan Chen et al [22] study multimedia streaming over cognitive radio networks and propose three auction-based schemes (ACA-S, ACA-T, and ACA-A) to distributively allocate the spectrum. Although those studies [18][19][20][21][22] discuss resource allocation for multimedia traffic transmissions, their schemes are not suitably applied to SVC-encoded multimedia streams because they only study the issue of frame transmission delay but do not take frame priorities (such as the priority structure resulting from the encoding scheme of SVC) into consideration. SVC was standardized by the Joint Video Team (JVT) and ITU-T Video Coding Experts Group (VCEG) in 2007, and is still a new standard.…”

Section: Introductionmentioning

confidence: 99%

Game‐based cross‐layer channel allocation with SVC‐encoded multimedia streams in cognitive radio networks

Kuo

2012

Int J Network Mgmt

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The available unlicensed spectrum is increasingly being used by new wireless technologies, but past measurements show that the licensed spectrum is extremely underutilized. To address this issue, the IEEE 802.22 Working Group is developing a novel wireless air interface standard based on cognitive radios (CRs), i.e. IEEE 802.22 wireless regional area networks (WRANs). Moreover, over the last decade wireless multimedia applications have developed rapidly, raising significant concerns about the quality of service (QoS) of multimedia stream transmissions. In particular, the Joint Video Team (JVT) and ITU-T Video Coding Experts Group (VCEG) jointly proposed Scalable Video Coding (SVC) as the next-generation multimedia compression standard. However, the current IEEE 802.22 WRAN draft does not specify QoS mechanisms for SVC-encoded multimedia stream transmission in CR networks. To resolve this problem, we developed a cross-layer channel allocation algorithm (CLCAA) and a novel media access control (MAC) protocol to work with the algorithm. The CLCAA adapts to the characteristics of multimedia traffic and variations of wireless channels by determining the weighting of source-destination pair, which is determined by the deadlines of SVC-encoded multimedia streams, the queuing delay and channel conditions. The CLCAA then allocates transmission opportunities to source-destination pairs based on their weightings and game theory. We also conducted extensive simulations to demonstrate the efficiency of the CLCAA scheme. The simulation results show that the CLCAA scheme not only guarantees QoS for multimedia traffic but also achieves fairness across different streams.

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Gunawardena

Zhuang

2014

Modeling and Analysis of Voice and Data in Cognitive Radio Networks

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Cross-layer design of cognitive radio network for real time video streaming transmission

Cited by 6 publications

References 9 publications

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Game‐based cross‐layer channel allocation with SVC‐encoded multimedia streams in cognitive radio networks

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