Flow-level traffic model for adaptive streaming services in mobile networks

2018 ◽  
Vol 137 ◽  
pp. 1-16 ◽  
Author(s):  
Yu-Ting Lin ◽  
Thomas Bonald ◽  
Salah Eddine Elayoubi
Keyword(s):  
Mobile Networks ◽  
Traffic Model ◽  
Adaptive Streaming ◽  
Streaming Services

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals Edge Computing Assisted Adaptive Streaming Scheme for Mobile Networks

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 2142-2152
Author(s):  
Minsu Kim ◽  
Kwangsue Chung
Keyword(s):  
Mobile Networks ◽  
Edge Computing ◽  
Adaptive Streaming

Analysis of Managed and Over-the-Top Streaming Services in Mobile Networks

2012 ◽  
Vol 35 (3) ◽  
Author(s):  
Jochen Eisl ◽  
Gerhard Kuhn ◽  
Matthias Lott ◽  
Martin Varela ◽  
Jarmo Prokkola ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Streaming Services ◽  
Over The Top

scholarly journals Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

2020 ◽  
Vol 10 (5) ◽  
pp. 1793
Author(s):  
Lina Du ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Jing Zhang ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Feature Vector ◽  
Video Quality ◽  
Superior Performance ◽  
Adaptive Streaming ◽  
Input Feature ◽  
Streaming Services ◽  
Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

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