7 research outputs found
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
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
Modeling Continuous Video QoE Evolution: A State Space Approach
A rapid increase in the video traffic together with an increasing demand for
higher quality videos has put a significant load on content delivery networks
in the recent years. Due to the relatively limited delivery infrastructure, the
video users in HTTP streaming often encounter dynamically varying quality over
time due to rate adaptation, while the delays in video packet arrivals result
in rebuffering events. The user quality-of-experience (QoE) degrades and varies
with time because of these factors. Thus, it is imperative to monitor the QoE
continuously in order to minimize these degradations and deliver an optimized
QoE to the users. Towards this end, we propose a nonlinear state space model
for efficiently and effectively predicting the user QoE on a continuous time
basis. The QoE prediction using the proposed approach relies on a state space
that is defined by a set of carefully chosen time varying QoE determining
features. An evaluation of the proposed approach conducted on two publicly
available continuous QoE databases shows a superior QoE prediction performance
over the state-of-the-art QoE modeling approaches. The evaluation results also
demonstrate the efficacy of the selected features and the model order employed
for predicting the QoE. Finally, we show that the proposed model is completely
state controllable and observable, so that the potential of state space
modeling approaches can be exploited for further improving QoE prediction.Comment: 7 pages, 3 figures, conferenc
Perceptual Quality-of-Experience: Modeling and Optimization for Adaptive Video Streaming
In this thesis, we address the problem of understanding and modeling the streaming QoE as perceived subjectively by the users. Streaming QoE modeling is challenging as it is continuous, dynamic, and time-varying in nature. Hence, it is important that the QoE is evaluated in a continuous manner so that suitable actions can be taken at the network controller such as eNodeB in cellular networks so that the QoE degradation of the video users is minimized
A linear regression framework for assessing time-varying subjective quality in HTTP streaming
In an HTTP streaming framework, continuous time quality evaluation is necessary to monitor the time-varying subjective quality (TVSQ) of the videos resulting from rate adaptation. In this paper, we present a novel learning framework for TVSQ assessment using linear regression under the Reduced-Reference (RR) and the No-Reference (NR) settings. The proposed framework relies on objective short time quality estimates and past TVSQs for predicting the present TVSQ. Specifically, we rely on spatio-temporal reduced reference en-tropic differencing for RR and on a 3D convolutional neural network for NR quality estimations. While the proposed RR-TVSQ model delivers competitive performance with state-of-the-art methods, the proposed NR-TVSQ model outperforms state-of-the-art algorithms over the LIVE QoE database
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
Due to the rate adaptation in hypertext transfer protocol adaptive streaming, the video quality delivered to the client keeps varying with time depending on the end-to-end network conditions. Moreover, the varying network conditions could also lead to the video client running out of the playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). Hence, it is important to quantify the perceptual QoE of the streaming video users and to 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. Toward this end, we present long short-term memory (LSTM)-QoE, a recurrent neural network-based QoE prediction model using an 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 datasets, 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 an excellent performance across these datasets. Furthermore, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for the QoE prediction
A Continuous QoE Evaluation Framework for Video Streaming over HTTP
A continuous evaluation of the end user’s Qualityof- Experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time varying channel quality to its users. In Hyper Text Transfer Protocol (HTTP) based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are two-fold. Firstly, we present a database consisting of videos at Full High Definition and Ultra High Definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Secondly, we present a QoE evaluation framework comprising a learning based model during playback and an exponential model during rebuffering. Further, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http://www.iith.ac.in/~lfovia/downloads.html