scholarly journals A dynamic almost blank subframe scheme for video streaming traffic model in heterogeneous networks

2015 ◽  
Author(s):  
A. Daeinabi ◽  
K. Sandrasegaran ◽  
Sh. Barua
Keyword(s):  
Video Streaming ◽  
Heterogeneous Networks ◽  
Traffic Model ◽  
Streaming Traffic ◽  
Almost Blank Subframe

Mobility Management for Video Streaming on Heterogeneous Networks

2010 ◽  
Vol 17 (1) ◽  
pp. 35-35 ◽  
Author(s):  
Chung-Ming Huang ◽  
Chung-Wei Lin ◽  
Chia-Ching Yang
Keyword(s):  
Video Streaming ◽  
Heterogeneous Networks ◽  
Mobility Management

scholarly journals Building Reproducible Video Streaming Traffic Generators

2021 ◽  
Author(s):  
Calvin Ardi ◽  
Alefiya Hussain ◽  
Stephen Schwab
Keyword(s):  
Video Streaming ◽  
Streaming Traffic

Pricing framework for almost blank subframe scheme in two-tier heterogeneous networks

2017 ◽  
Vol 31 (3) ◽  
pp. e3454 ◽  
Author(s):  
Li Li ◽  
Zhaorong Zhou ◽  
Yanjun Hu ◽  
Tao Jiang ◽  
Menghan Wei
Keyword(s):  
Heterogeneous Networks ◽  
Almost Blank Subframe ◽  
Pricing Framework

scholarly journals Investigating Redundant Internet Video Streaming Traffic on iOS Devices: Causes and Solutions

2014 ◽  
Vol 16 (2) ◽  
pp. 510-520 ◽  
Author(s):  
Yao Liu ◽  
Qi Wei ◽  
Lei Guo ◽  
Bo Shen ◽  
Songqing Chen ◽  
...  
Keyword(s):  
Video Streaming ◽  
Internet Video ◽  
Streaming Traffic

Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

2021 ◽  
Vol 48 (4) ◽  
pp. 33-36
Author(s):  
Özge Celenk ◽  
Thomas Bauschert ◽  
Marcus Eckert
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Video Streaming ◽  
Quality Of Experience ◽  
Service Providers ◽  
Early Stage ◽  
Traffic Information ◽  
Streaming Traffic ◽  
Testing Accuracy

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.

Sign in / Sign up

Export Citation Format

Share Document