Wireless Adapter Sleep Scheduling Based on Video QoE: How to Improve Battery Life When Watching Streaming Video?

Csernai, Márton; Gulyás, András

doi:10.1109/icccn.2011.6005796

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…Depending on the determination of the appropriate QoE IFs which may need to be adjusted for a given service, QoE optimization may be performed at different locations, as depicted in Figure 2. Thus, as summarized in Table 4, the optimization can be conducted by applying various control mechanisms at the base stations within various access networks [109][110][111] by applying policy management rules at the gateways or routers within the core network [97,103,112,113], by conducting adjustments at the servers in the service/application [104,113,114], content or cloud domains, or the combination thereof [107], as well as on end-user device [115]. Since QoE control relies on QoE monitoring and measurement information, usually the control locations are the same as monitoring and measurement points.…”

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confidence: 99%

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Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

116

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With the move towards converged all-IP wireless network environments, managing end-user Quality of Experience (QoE) poses a challenging task, aimed at meeting high user expectations and requirements regarding reliable and cost-effective communication, access to any service, anytime and anywhere, and across multiple operator domains. In this paper, we give a survey of state-of-the-art research activities addressing the field of QoE management, focusing in particular on the domain of wireless networks and addressing three management aspects: QoE modeling, monitoring and measurement, and adaptation and optimization. Furthermore, we identify and discuss the key aspects and challenges that need to be considered when conducting research in this area.

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confidence: 99%

“…The addressed approaches propose various QoE optimization strategies that are aimed at optimal network resource allocation [100,101,112,113]; optimal radio resource allocation [97,103,107,109]; service optimization [97,100,102,107,110,114]; optimized handover decision [113]; access network selection [111]; or battery consumption [115].…”

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confidence: 99%

Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

116

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“…As stated before, QoE optimization can be performed at various points using different QoE mechanisms, for example at the base stations within the access networks (e.g., using optimal network and radio resource allocation) [87]- [91]. It can be also performed by conducting adjustments of servers in the service/application [71], [91]- [93], applying QoE-based policy management within the core networks [67], [68], [90], [91], or an optimized handover decision [91], on end-user device (e.g., battery consumption) [94]. In addition, QoE optimization can be performed at different levels ranging from link to application-layer [95], [64], or using a common crosslayer approach [67], [68], [90], [91], [83], [96].…”

Section: E Summarymentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

143

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The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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“…The work in [21]presents a framework that reduces the energy consumption of wireless mobile devices during streaming video content over Wireless LAN networks by utilizing the power saving mechanisms defined in the IEEE 802.11e standard. The framework allows video applications to directly optimize the overall energy efficiency by controlling the sleep cycles of wireless network adapters based on video QoE.…”

Section: Qoe Evaluation and Power-driven Video Quality On Iotmentioning

confidence: 99%