An Edge-to-Cloud Virtualized Multimedia Service Platform for 5G Networks

Álvarez, Federico; Breitgand, David; Griffin, David; Andriani, Pasquale; Rizou, Stamatia; Zioulis, Nikolaos; Moscatelli, Francesca; Serrano, Javier; Keltsch, Madeleine; Trakadas, Panagiotis; Phan, Truong Khoa; Weit, Avi; Acar, Ugur; Prieto, Óscar; Iadanza, Francesco; Carrozzo, Gino; Koumaras, Harilaos; Zarpalas, Dimitrios; Jiménez, David

doi:10.1109/tbc.2019.2901400

Cited by 70 publications

(29 citation statements)

References 21 publications

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“…1. Some of the wireless multimedia applications providing the basis of this huge volume of data can be named such as immersive 360°video, distributed gaming, free-viewpoint video, augmented reality (AR), virtual reality (VR), and extended reality (XR) [2]. Obviously, compared to the traditional services, these multimodal applications require a higher level of quality of service (QoS) and quality of experience (QoE).…”

Section: A Backgroundmentioning

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: A Backgroundmentioning

confidence: 99%

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

Piran¹

2020

Preprint

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“…The authors in reference [10] and reference [11] show that the characteristics might differ significantly when comparing existing MANO solutions. It may be necessary, depending on the targeted use-case, to augment an existing MANO platform and integrate new functionalities ( [10,12]).…”

Section: Related Workmentioning

confidence: 99%

The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services

et al. 2019

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The emergence of widespread cloudification and virtualisation promises increased flexibility, scalability, and programmability for the deployment of services by Vertical Service Providers (VSPs). This cloudification also improves service and network management, reducing the Capital and Operational Expenses (CAPEX, OPEX). A truly cloud-native approach is essential, since 5G will provide a diverse range of services - many requiring stringent performance guarantees while maximising flexibility and agility despite the technological diversity. This paper proposes a workflow based on the principles of build-to-order, Build-Ship-Run, and automation; following the Next Generation Platform as a Service (NGPaaS) vision. Through the concept of Reusable Functional Blocks (RFBs), an enhancement to Virtual Network Functions, this methodology allows a VSP to deploy and manage platforms and services, agnostic to the underlying technologies, protocols, and APIs. To validate the proposed workflow, a use case is also presented herein, which illustrates both the deployment of the underlying platform by the Telco operator and of the services that run on top of it. In this use case, the NGPaaS operator facilitates a VSP to provide Virtual Network Function as a Service (VNFaaS) capabilities for its end customers.

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“…The main key-enablers to handle the requirements of this new era include flexible numerology, bandwidth parts (BWPs), service multiplexing and mini-slotting, optimized frame structure, massive MIMO, inter-networking between high and low bands, and ultra lean transmission [1], [6], [7]. In addition, emergent technologies like software-defined networking (SDN) [5], network function virtualization (NFV), and network slicing [4] will also be key technologies in paving the way for enhancements in 5G and beyond.…”

Section: Introductionmentioning

confidence: 99%

Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer

2020

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Network management tools are usually inherited from one generation to another. This was successful since these tools have been kept in check and updated regularly to fit new networking goals and service requirements. Unfortunately, new networking services will render this approach obsolete and handcrafting new tools or upgrading the current ones may lead to complicated systems that will be difficult to maintain and improve. Fortunately, recent advances in AI have provided new promising tools that can help solving many network management problems. Following this interesting trend, the current article presents LEASCH, a deep reinforcement learning model able to solve the radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is developed and trained in a sand-box and then deployed in a 5G network. It has been evaluated under different numerology settings. The experimental results show that it is both numerology-agnostic and efficient when compared to conventional baseline methods in many key performance indicators. INDEX TERMS 5G; MAC; deep reinforcement learning; scheduling; resource management.

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An Edge-to-Cloud Virtualized Multimedia Service Platform for 5G Networks

Cited by 70 publications

References 21 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

The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services

Learn to Schedule (LEASCH): A Deep Reinforcement Learning Approach for Radio Resource Scheduling in the 5G MAC Layer

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