Video Transcoding, Caching, and Multicast for Heterogeneous Networks Over Wireless Network Virtualization

Zhou, Yuchen; Yu, F. Richard; Chen, Jian; Kuo, Yan-Fu

doi:10.1109/lcomm.2017.2759780

Cited by 22 publications

(8 citation statements)

References 10 publications

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“…In addition, several research works have also verified the importance of ICN and MEC cooperation [67], [183]- [186]. A novel HetNets virtualization architecture with ICN and MEC techniques is proposed for video trans-coding, caching, and multi-cast in [183]. A virtual multi-resources allocation scheme is used in the designed framework to maximize the utility of computing, caching, and communication to support the massive content delivery.…”

Section: Information Centric Networkingmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

649

328

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The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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Section: Information Centric Networkingmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

649

328

View full text Add to dashboard Cite

show abstract

“…The experimental results reveal that a 10% improvement in video delivery latency may cause about 10% to 30% increase in the operational costs, depending on the network load. Zhou et al [257] study the joint optimization of video caching, transcoding, and communication resources in a virtualized HetNet. In particular, the costs of computing and caching are inversely related.…”

Section: E Joint Optimization Of Video Edge-c3 Resource Allocationmentioning

confidence: 99%

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Jedari

Premsankar

Illahi

et al. 2021

IEEE Commun. Surv. Tutorials

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“…In [144] the authors model the wireless channel, where the heterogeneous access points' capacities are derived from the dynamically allocated width of the spectrum and the signal strength. Similarly, radio transmit power [81], transmission data rate [85], momentary SINR [158], [159], wireless backhaul bandwidth partitioning [154], wireless link capacity [143] can be included in the optimization objective, or constraints when tackling the offloading decision making from the holistic system perspective.…”

Section: A Single Componentmentioning

confidence: 99%

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Video Transcoding, Caching, and Multicast for Heterogeneous Networks Over Wireless Network Virtualization

Cited by 22 publications

References 10 publications

Survey on Multi-Access Edge Computing for Internet of Things Realization

Survey on Multi-Access Edge Computing for Internet of Things Realization

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Survey on Placement Methods in the Edge and Beyond

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