Video Super-Resolution and Caching—An Edge-Assisted Adaptive Video Streaming Solution

Zhang, Aoyang; Li, Qing; Chen, Ying; Ma, Xiaoteng; Zou, Longhao; Jiang, Yong; Xu, Zhimin; Muntean, Gabriel‐Miro

doi:10.1109/tbc.2021.3071010

Cited by 44 publications

(16 citation statements)

References 37 publications

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“…Edge computing, as an emerging computing paradigm, provides an innovative opportunity for the synchronized computing of multisensor PPG signals in the proximity of the edge networks. Advancements in edge computing have the desired computing capacities for the rapid development of Internet of Tings (IoT) [1], Internet of Vehicles (IoV) [7,8], Satellite-Terrestrial Networks [9], reconfgurable wireless communications [10], and video streaming processing [11][12][13][14], especially in IoMT. Te explosive proliferation of IoMT generates massive amounts of time-series data, such as PPG signals.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Edge‐Enabled Heart Rate Estimation from Multisensor PPG Signals

Chen

Zhao

2023

Journal of Healthcare Engineering

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Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Edge‐Enabled Heart Rate Estimation from Multisensor PPG Signals

Chen

Zhao

2023

Journal of Healthcare Engineering

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“…In order to make use of a global view of the multi-client state, several network-assisted QoE-aware bitrate aggregation and joint optimization algorithms were proposed [24]- [30]. Cofano et al [24] allocates network bandwidth slices to video streams, or guides bitrate selections by using a network controller and Software Defined Network (SDN) switches.…”

Section: A Abr Solutions For Multimedia Transmissionsmentioning

confidence: 99%

“…However, such methods cannot realize fine-grained state tracking and control for many clients, so low bandwidth utilization and poor QoE caused by bandwidth competition still exist. Ma et al [29], Zhang et al [30] and Shi et al [31] propose QoE optimization frameworks for VoD services based on the smart edge. However, such models are not applicable to the low-latency demands of live streaming.…”

Section: A Abr Solutions For Multimedia Transmissionsmentioning

confidence: 99%

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Zou

et al. 2022

IEEE Trans. on Broadcast.

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Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel QoE-aware Adaptive Video bitrate Aggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a "smart edge" which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms

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“…This includes high quality 5G media services such as augmented reality (AR), virtual reality (VR) and other types of video content. These services require innovative network solutions for their distribution on one hand [2], [3] and increased efficiency for the battery-powered user devices on the other hand [4], [5] in order to maintain a high and smooth viewer experience.…”

Section: Introductionmentioning

confidence: 99%