A survey of QoE-driven video streaming over cognitive radio networks

Zhao, Heyun; Mao, Shiwen; Jiang, Tao

doi:10.1109/mnet.2015.7340420

Cited by 23 publications

(8 citation statements)

References 15 publications

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“…Published in 2012, the authors focused on transport protocols and algorithms devised for sensor networks and especially smart grid, 500KV substation, main power room, etc. He et al in [30] reviewed QoE for video streaming over CRNs in 2015. The authors in [31] reviewed various multimedia applications supported by CRNs, routing and link-layer protocols, QoE design, security requirements, white-spaces, TV white-spaces, and cross-layer design.…”

Section: B Review Of Related Survey Articlesmentioning

confidence: 99%

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: B Review Of Related Survey Articlesmentioning

confidence: 99%

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

Piran¹

2020

Preprint

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“…As an application layer performance metric, video quality directly reflect the user satisfaction level in contrast to physical layer metrics. In future mobile networks, it is more important to develop a cross-layer interference management approach that jointly considers the physical layer issues as well as the user's requirement and experience [12], [13]. Motivated by this observation, some cross-layer video transmission designs have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach

Zhang

Mao

2020

IEEE Access

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Device-to-device (D2D) communications have been regarded as a promising technology to meet the dramatically increasing video data demand in the 5G network. In this paper, we consider the power control problem in a multiuser video transmission system. Due to the non-convex nature of the optimization problem, it is challenging to obtain an optimal strategy. In addition, many existing solutions require instantaneous channel state information (CSI) for each link, which is hard to obtain in resource-limited wireless networks. We developed a multi-agent deep reinforcement learning-based power control method, where each agent adaptively controls its transmit power based on the observed local states. The proposed method aims to maximize the average quality of received videos of all users while satisfying the quality requirement of each user. After off-line training, the method can be distributedly implemented such that all the users can achieve their target state from any initial state. Compared with conventional optimization based approach, the proposed method is model-free, does not require CSI, and is scalable to large networks. INDEX TERMS Multiuser video transmission, multi-agent deep reinforcement learning, power control, quality of experience.

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“…The interference power at each primary BS is controlled with the interference temperature model [26]. Additionally, video traffic transmission is considered [27,28]. At each time slot, secondary MTs allocate power, P k nsm , for cognitive network n BS s MT m at subcarrier k and video packet scheduling decision, x f l nsm .…”

Section: Introductionmentioning

confidence: 99%

Video Packet Scheduling With Stochastic QoS for Cognitive Heterogeneous Networks

Nallanathan

Yang

2017

IEEE Trans. Veh. Technol.

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A survey of QoE-driven video streaming over cognitive radio networks

Cited by 23 publications

References 15 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

Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach

Video Packet Scheduling With Stochastic QoS for Cognitive Heterogeneous Networks

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