A New Efficient Architecture for Adaptive Bit-Rate Video Streaming

Waheed, Muhammad; Jamil, Faisal; Qayyum, Amir; Jamil, Harun; Cheikhrouhou, Omar; Ibrahim, M.B.; Bhushan, Bharat; Hamam, Habib

doi:10.3390/su13084541

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Mobile Information Systems framework was proposed to optimize 360-degree video streaming from the perspective of viewport prediction, prefetching scheduling, and rate adaptation. In [22], a design is effective in streaming, distribution, and caching multimedia content was proposed. In [23], a new energy efficiency method based on experience quality was proposed to distribute peerto-peer video in mobile ad hoc networks.…”

Section: Related Researchmentioning

confidence: 99%

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Wang

2022

Mobile Information Systems

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The rapid development of Olympic Games and the intensification of professional sports events make the competition between excellent athletes and teams become more and more fierce, so the demands on athletes’ psychology, techniques, and physical strength are becoming higher and higher, especially that the demands on physical strength are more prominent. Therefore, physical training is an important part of sports training and the core link of competitive sports, which is widely valued by coaches and athletes all over the world. At the same time, with the influence and penetration of the rapid development of modern information technology and network big data on competitive sports, the scientific and digital process of athletes’ physical training has also been accelerated, and many new ideas, scientific training methods, and advanced sports technology have emerged. During the pandemic, many aggregation activities have been disrupted, as well as physical training. Therefore, this paper improves the quality of physical training through video, so that athletes can do physical training at any time and any place. A cut vertices spanning tree algorithm based on machine learning is proposed to distribute layered multicast streaming and dynamically adjust the number of layers of sports video streaming. The cut vertices spanning tree algorithm is mainly applied to the situation when the network bandwidth resources are relatively scarce. The evaluation results indicate that the proposed method can improve the estimated quality of experience (QoE), packet loss rate, link utilization, and video delay on Mininet simulation platform. Furthermore, it can be seen from the experiments that the proposed method has a good performance on distribution of sports video stream assisting physical training.

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Section: Related Researchmentioning

confidence: 99%

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Wang

2022

Mobile Information Systems

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“…The internet of things is an evolving field, attracting much attention from the research community [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. The systems pertaining to location tracking and intelligent navigation are widely employed to handle specific scenarios related to security, logistic medicine, and mission-critical indoors.…”

Section: Related Workmentioning

confidence: 99%

Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Jamil

Qayyum

Jamil

et al. 2021

Sensors

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This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

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“…This model did not utilize deep reinforcement learning by double Q-values against end users. Waheed et al [23] implemented an efficient architecture for an adaptive bitrate video, that authorized efficient and effective streaming, caching, and distribution of the multimedia content. The experiments were carried out for factors impacting QoE and the outcomes were further evaluated and analyzed.…”

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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A New Efficient Architecture for Adaptive Bit-Rate Video Streaming

Cited by 5 publications

References 43 publications

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

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

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