Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

07/18/2018
by   Nagabhushan Eswara, et al.
0

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/16/2018

Modeling Continuous Video QoE Evolution: A State Space Approach

A rapid increase in the video traffic together with an increasing demand...
research
10/27/2021

End-to-end LSTM based estimation of volcano event epicenter localization

In this paper, an end-to-end based LSTM scheme is proposed to address th...
research
03/20/2020

Continuous QoE Prediction Based on WaveNet

Continuous QoE prediction is crucial in the purpose of maximizing viewer...
research
03/19/2020

Convolutional Neural Networks for Continuous QoE Prediction in Video Streaming Services

In video streaming services, predicting the continuous user's quality of...
research
07/10/2017

An Augmented Autoregressive Approach to HTTP Video Stream Quality Prediction

HTTP-based video streaming technologies allow for flexible rate selectio...
research
07/14/2021

A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer

Recurrent neural networks for language models like long short-term memor...
research
09/03/2021

Estimating Demand Flexibility Using Siamese LSTM Neural Networks

There is an opportunity in modern power systems to explore the demand fl...

Please sign up or login with your details

Forgot password? Click here to reset