Window‐based adaptive technique for real‐time streaming of scalable video over cognitive radio networks

Omer, Ala Eldin; Hassan, Mohamed S.; El-Tarhuni, Mohamed

doi:10.1049/iet-com.2016.0929

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…The cross-layer incongruity with pattern, topology and spatio-temporal features leads to the degradation of network performance with a series of specific problems such as link failure, end-to-end delay, interactive instability, routing congestion, etc. The current approaches include delay tolerant network, wireless mesh network, content delivery network [46], ultra-dense network [47], cognitive radio network [48], peer-to-peer, machine-to-machine [49], device-to-device [50], and so on.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Sheng

2018

IEEE Access

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In order to improve future network performance, this paper proposes scalable intelligence-enabled networking (SIEN) with eliminating traffic redundancy for audio-visual-tactile Internet in 5G scenarios such as enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The SIEN consists of an intelligent management plane (ImP), an intelligence-enabled plane (IeP), a control plane and a user plane. For the ImP, the containers with decision execution are constructed by a novel graph algorithm to organize objects such as network elements and resource partitions. For the IeP, a novel learning system is designed with decision making using a congruity function for generalization and personalization in the presence of imbalanced, conflicting and partial data. For the control plane, a scheme of identifier-locator mapping is designed by referring to information-centric networking and software-defined networking. For the user plane, the registrations, requests and data are forwarded to implement the SIEN and test its performance. The evaluation shows the SIEN outperforms four state-of-the-art techniques for redundant traffic reduction by up to 46.04% based on a mix of assumption, simulation and proof-of-concept implementation for audiovisual-tactile Internet multimedia service. To confirm the validity, the best case and the worst case for traffic offloading are tested with the data rate, the latency and the density. The evaluation only focused on the scalability issue, while the SIEN would be beneficial to improve more issues such as inter-domain security, ultra-low latency, on-demand mobility, multihoming routing, and cross-layer feature incongruity.

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Section: Analysis and Discussionmentioning

confidence: 99%

Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Sheng

2018

IEEE Access

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“…Therefore, in what follows, we shed light on the requirements of video streaming applications and common techniques in the literature for resource allocation in CRNs. In [16], [17], the authors focused on streaming scalable encoded video-on-demand over CRNs in an infrastructure-based CR system. They proposed a channel allocation scheme that jointly considers the status of the playback buffers at the SUs and the quality of the available channels while allocating channels among active SUs.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Allocation Techniques for Cognitive Radio Networks

2022

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The remarkable growth in the demand for multimedia streaming over wireless networks is challenged by spectrum scarcity. To mitigate the effect of such a challenge, Cognitive Radio Networks (CRNs) were introduced as a promising technology since CRNs offer a great advantage to unlicensed users, also known as secondary users (SUs), by allowing them to opportunistically access the licensed bands when these bands are not used by their primary users (PUs). In this work, an LTE-based CRN is proposed with the objective of guaranteeing continuous video playback at the SUs end at acceptable perceptual quality. To accomplish this objective, different resource allocation schemes are introduced to adaptively assign the available channels to SUs while considering the quality of these channels as well as the buffer occupancies of the different SUs. In addition, a streaming algorithm is proposed to ensure the delivery of the base and enhancement layers of the scalable video frames within the delay constraints with priority given to the baselayers of the video frames to guarantee the continuity of video playback. Furthermore, adaptive modulation is used based on the channel state information (CSI) as fed-back by the SUs. The performance of the proposed schemes is evaluated through extensive Monte-Carlo simulations.INDEX TERMS Cognitive radio networks, Markov chains, primary radio user activity, resource allocation, LTE.

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QoE-Driven Resource Allocation for Live Video Streaming Over D2D-Underlaid 5G Cellular Networks

2018

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Window‐based adaptive technique for real‐time streaming of scalable video over cognitive radio networks

Cited by 7 publications

References 28 publications

Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Scalable Intelligence-Enabled Networking with Traffic Engineering in 5G Scenarios for Future Audio-Visual-Tactile Internet

Spectrum Allocation Techniques for Cognitive Radio Networks

QoE-Driven Resource Allocation for Live Video Streaming Over D2D-Underlaid 5G Cellular Networks

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