QoE-Aware Dual Control System to Guarantee Battery Lifetime for Mobile Video Applications

Groba, Angel; Lobo, Pedro J.; Chavarrías, Miguel

doi:10.1109/tce.2019.2932778

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…[165], [166]. Thus, the overall energy consumption at the decoder (particularly in the case of mobile devices), has become a vital parameter that affects the overall end-user QoE in multimedia applications [167], [168].…”

Section: Impact Of Decoder Optimizations On the Qoementioning

confidence: 99%

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

et al. 2022

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The multimedia delivery chain consists of multiple stages such as content preparation, content delivery via Over-The-Top delivery network and Internet Service Providers network. Within the multimedia service chain, each stage influences the Quality of Experience (QoE) of the end user. The objective of this work is to provide a comprehensive literature survey with future research challenges and opportunities in the field of time-varying video quality in multimedia service delivery. The contribution of this work is two fold: 1) Survey -we provide a review of state-of-the-art works for video quality models to quantify multiple artifacts into a single QoE metric, pooling strategies for global quality measurements, and Continuous Time-Varying Quality (CTVQ) models; 2) Future Challenges and Directions -we investigate ten major research challenges and future directions based on the state-of-the-art for QoE modelling, QoE-aware encoding/decoding and QoE monitoring/management of multimedia streaming in next-generation networks.

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Section: Impact Of Decoder Optimizations On the Qoementioning

confidence: 99%

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

et al. 2022

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“…In Li-GD algorithm, for each round of iteration, we need to calculate the gradient for 𝐵 # and 𝑟 # , and the number of mobile users is 𝑈. Moreover, the gradient calculation of 𝐵 # , which is shown in (35), is much more complex than 𝑟 # . The (35) can be rewritten as:…”

Section: Ieee Transactions On Journal Name Manuscript Idmentioning

confidence: 99%

“…Moreover, the gradient calculation of 𝐵 # , which is shown in (35), is much more complex than 𝑟 # . The (35) can be rewritten as:…”

Section: Ieee Transactions On Journal Name Manuscript Idmentioning

confidence: 99%

“…For the differentiable function 𝑓(𝑥) , if 𝑓"(𝑥) > 0 , it is convex [51]. The 𝑓′(𝑥) is presented in (35). Thus, let ℒ =…”

Section: Ieee Transactions On Journal Name Manuscript Idmentioning

confidence: 99%

“…Additionally, for the mobile device, one of the most important factors to consider is the energy consumption, because it is always powered by battery which is limited on energy store and decides the lifetime of devices. In most scenarios of MEC, the lifetime of mobile devices is more important than inference delay and accuracy to some extent, because long lifetime is the foundation of high QoS [33][34][35]. Once the mobile devices are out of energy, the QoS is meaningless.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fair Computation Efficiency Scheduling in NOMA-Aided Mobile Edge Computing

Huang

Zeng

et al. 2020

IEEE Wireless Commun. Lett.

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Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G, can achieve massive connections and high spectrum efficiency. Motivated by the benefits of NOMA, integrating NOMA with model split in MEC to reduce the inference latency further becomes attractive. However, the NOMA based communication during split inference has not been properly considered in previous works. Therefore, in this paper, we integrate the NOMA into split inference in MEC, and propose the effective communication and computing resource allocation algorithm to accelerate the model inference at edge. Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, and the variables of subchannel allocation and model split are discrete, the gradient descent (GD) algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is proposed to reduce the complexity of GD algorithm that caused by the parameter discrete. Additionally, the properties of the proposed algorithm are also investigated, which demonstrate the effectiveness of the proposed algorithms.

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Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients

Díaz

Fernández

Sacristan

et al. 2020

IEEE Trans. Consumer Electron.

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QoE-Aware Dual Control System to Guarantee Battery Lifetime for Mobile Video Applications

Cited by 4 publications

References 27 publications

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

Fair Computation Efficiency Scheduling in NOMA-Aided Mobile Edge Computing

Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients

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