TY - GEN
T1 - Video QoE Inference with Machine Learning
AU - Tisa-Selma,
AU - Bentaleb, Abdelhak
AU - Harous, Saad
N1 - Funding Information:
This research work is supported by UAEU Grant: 31T102-UPAR-1-2017.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.
AB - HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.
KW - Deep learning
KW - Encrypted video traffic
KW - Inferring QoE
KW - Machine learning
KW - MLPB
KW - QoE
KW - SOM
UR - http://www.scopus.com/inward/record.url?scp=85125622157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125622157&partnerID=8YFLogxK
U2 - 10.1109/IWCMC51323.2021.9498579
DO - 10.1109/IWCMC51323.2021.9498579
M3 - Conference contribution
AN - SCOPUS:85125622157
T3 - 2021 International Wireless Communications and Mobile Computing, IWCMC 2021
SP - 1048
EP - 1053
BT - 2021 International Wireless Communications and Mobile Computing, IWCMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Wireless Communications and Mobile Computing, IWCMC 2021
Y2 - 28 June 2021 through 2 July 2021
ER -