Analysis of capacity of picocell with dominating video streaming traffic

2014 ◽  
Author(s):  
Evgeny Bakin ◽  
Anna Borisovskaya ◽  
Igor Pastushok
Keyword(s):  
Video Streaming ◽  
Streaming Traffic

scholarly journals Building Reproducible Video Streaming Traffic Generators

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

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.

Scalable Video Streaming Traffic Delivery in IP/UMTS Networking Environments

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
Thomas Pliakas ◽  
George Kormentzas ◽  
Charalabos Skianis
Keyword(s):  
Video Streaming ◽  
Scalable Video ◽  
Streaming Traffic ◽  
Scalable Video Streaming

scholarly journals Video Streaming Service Identification on Software-Defined Networking

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Luis Miguel Castañeda Herrera ◽  
Wilmar Yesid Campo-Muñoz ◽  
Alejandra Duque Torres
Keyword(s):  
Video Streaming ◽  
Network Traffic ◽  
Network Performance ◽  
Video On Demand ◽  
Traffic Classification ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Video Traffic ◽  
Tree Model ◽  
Streaming Traffic

It is well known that video streaming is the major network traffic today. Futhermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, SoftwareDefined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm that takes advantage of Artificial Intelligence (AI) by using Machine Learning approaches, which allows integrating behavioural models to detect patterns, like video streaming traffic identification, in SDN traffic. In our initial proof-of-concept, we derive the relevant information of network traffic in the form of flows statistics. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.

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