Deep reinforcement learning based QoE-aware actor-learner architectures for video streaming in IoT environments

Naresh, Mandan; Das, Vikramjeet; Saxena, Paresh; Gupta, Manik

doi:10.1007/s00607-021-01046-1

Cited by 13 publications

(5 citation statements)

References 46 publications

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“…The comparative analysis of the RNN‐LSTM is executed by considering the available QoE‐based video streaming delivery techniques, such as QAVA DRL, 14 A3C, 3 LSTM ANN, 29 and CNN‐QoE 4 …”

Section: Resultsmentioning

confidence: 99%

“…However, this model did not consider utilizing a network‐based optimization rather than the session‐based ones to improve the performance. Naresh et al 3 devised an actor‐learner architecture based on asynchronous advantage actor‐critic (A3C) for streaming videos in the IoT environment. Here, two sophisticated A3c methods, such as averaged A3C and follow then forage exploration (FFE) were integrated and employed for producing adaptive bitrates.…”

Section: Motivationmentioning

confidence: 99%

“…The QAVA‐DRL developed in reference 14 determined QoE based on the count of skipped chunks but it did not consider analyzing the effect of the skipped chunks on users' QoE by utilizing fine‐grained video features. Further, this approach can't be employed in virtual reality (VR) and augmented reality (AR) steaming applications directly. In reference 3, the A3C‐based approach was proposed and this scheme produced a substantially high QoE. However, the method did not include techniques, such as constrained application protocol (CoAP) for increasing efficiency. The LSTM ANN proposed in reference 29 was effective in streaming videos at high quality with minimal buffering time.…”

Section: Motivationmentioning

confidence: 99%

“…Research shows that video streaming applications, like YouTube and Netflix, contribute to more than 70% of online traffic 1,2 . With the increase in the utilization of mobile devices and smartphones, there has been a rapid growth in video streaming applications, and this makes it mandatory to provide high‐quality video streaming services consistently irrespective of the service providers 3 . In the meantime, there has been a growing demand from users for high‐quality videos.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

Venkatesh Naik,

Madhavi

2024

Concurrency and Computation

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SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

Venkatesh Naik,

Madhavi

2024

Concurrency and Computation

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“…In machine learning (ML)-based multimedia video streaming, the prediction accuracy is majorly dependent on the parameter setting as well as false results that are produced. Naresh et al [16] implemented an asynchronous actor-critic (A3C) with actor-learner architecture for the development of adaptive bit rates for video streaming in the environment of internet of things (IoT). This method combined the follow then forage exploration (FFE) and average A3C algorithms to solve the high variance in value estimates.…”

Section: Introductionmentioning

confidence: 99%

Bayesian K-means clustering based quality of experience aware multimedia video streaming

Peddareddygari Bayya Reddy,

Shagathur Narayanappa

2024

IJEECS

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<span>Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.</span>

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Trust-aware task offloading for cost-effective UAV-based edge computing based on reinforcement learning

Liu,

Xie,

Lin

et al. 2024

Neural Comput & Applic

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Deep reinforcement learning based QoE-aware actor-learner architectures for video streaming in IoT environments

Cited by 13 publications

References 46 publications

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

Bayesian K-means clustering based quality of experience aware multimedia video streaming

Trust-aware task offloading for cost-effective UAV-based edge computing based on reinforcement learning

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