QoE-aware video streaming for SVC over multiuser MIMO–OFDM systems

Li, Maodong; Chen, Zhenzhong; Tan, Peng Hui; Sun, Sumei; Tan, Yap‐Peng

doi:10.1016/j.jvcir.2014.10.011

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…The authors in [65] propose a QoE-aware video streaming solution to maximize multiuser QoE for Scalable Video Coding (SVC) streaming over multiuser (MU) Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed approach is achieved by combining QoE-aware video adaptation (QoEVA) and QoE-aware resource allocation (QoERA) schemes.…”

Section: A Quality Driven Schedulingmentioning

confidence: 99%

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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We present in this paper a comprehensive review and comparison of recent downlink scheduling approaches for video streaming traffic over the Orthogonal Frequency Division Multiple Access (OFDMA) based Long-Term Evolution (LTE) wireless technology. Focusing on content-aware downlink scheduling approaches, we provide an extensive literature review, a taxonomy for content-aware and content-unaware downlink schedulers, and tables that summarize the key approaches and common parameters among the schedulers. In addition, we analyze and compare via simulation the performance of some of the most relevant scheduling rules. Our main goal is to compare and analyze different classes of scheduling strategies in terms of network centric performance metrics as well as user centric metrics. Quality of Service (QoS) evaluation involves the evaluation of network performance parameters, e.g., packet loss rate, average system throughput and end-to-end packet delay. On the other hand, Quality of Experience (QoE) reflects the user's experience and satisfaction in terms of Mean Opinion Score (MOS). According to simulation results, proxy based QoE aware scheduling strategies perform best in terms of number of satisfied users and should be used in an LTE downlink to offer high quality video streaming services.

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Section: A Quality Driven Schedulingmentioning

confidence: 99%

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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Remote Radio Head Scheduling in LTE-Advanced Networks

Ramdev

Bajaj

Sidhu

2021

Wireless Pers Commun

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Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

Danish¹,

Alshamrani²

2017

Advances in Wireless Technologies and Telecommunication

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Video streaming is expected to acquire a massive share of the global internet traffic in the near future. Meanwhile, it is expected that most of the global traffic will be carried over wireless networks. This trend translates into considerable challenges for Service Providers (SP) in terms of maintaining consumers' Quality of Experience (QoE), energy consumption, utilisation of wireless resources, and profitability. However, the majority of Radio Resource Allocation (RRA) algorithms only consider enhancing Quality of Service (QoS) and network parameters. Since this approach may end up with unsatisfied customers in the future, it is essential to develop innovative RRA algorithms that adopt a user-centric approach based on users' QoE. This chapter focus on wireless video over Critical communication systems that are inspired by QoE perceived by end users. This chapter presents a background to introduce the reader to this area, followed by a review of the related up-to-date literature.

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QoE-aware video streaming for SVC over multiuser MIMO–OFDM systems

Cited by 3 publications

References 40 publications

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Remote Radio Head Scheduling in LTE-Advanced Networks

Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

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