Learning a Continuous-Time Streaming Video QoE Model

Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C.

doi:10.1109/tip.2018.2790347

Cited by 59 publications

(39 citation statements)

References 41 publications

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“…Hence, we employ STSQ as the only input feature in the proposed model for this database. A similar setting has been employed for QoE prediction in [6]. Alternately, the feature PI can be set to 1 meaning 'ON' and T R constant throughout the video duration in our proposed model for this database.…”

Section: A Database Descriptionmentioning

confidence: 99%

“…We also compare the median QoE prediction performances obtained by the proposed LSTM-QoE model with that of TV-QoE [6] on the LIVE Netflix [7] and the LIVE Mobile Video Stall-II [47] Databases. For a fair comparison, we employ a training-test split of 80/20 as considered in [6] for evaluation over both these databases. Upon the LIVE Netflix Database, we conduct the evaluation on two sets separately, as performed in [6]: 1) V c : the set of videos having compression artifacts only and 2) V s : the set of videos having both compression and stalling (rebuffering) artifacts.…”

Section: E Lstm-qoe Evaluationmentioning

confidence: 99%

“…For a fair comparison, we employ a training-test split of 80/20 as considered in [6] for evaluation over both these databases. Upon the LIVE Netflix Database, we conduct the evaluation on two sets separately, as performed in [6]: 1) V c : the set of videos having compression artifacts only and 2) V s : the set of videos having both compression and stalling (rebuffering) artifacts. The median QoE prediction performances on V s and V c video sets are tabulated in Tables V and VI, For the proposed model, while STRRED as the STSQ measure performs the best on the LIVE Netflix and the LIVE QoE Databases, NIQE emerges as the best performing VQA metric on the LFOVIA QoE Database.…”

Section: E Lstm-qoe Evaluationmentioning

confidence: 99%

“…QoE prediction performance comparison of LSTM-QoE against TV-QoE[6] over the V c set videos of the LIVE Netflix Database[7] with 80/20 split. The best performing results are indicated in bold.…”

mentioning

confidence: 99%

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

Eswara

Ashique

Panchbhai

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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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.

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Section: A Database Descriptionmentioning

confidence: 99%

Section: E Lstm-qoe Evaluationmentioning

confidence: 99%

Section: E Lstm-qoe Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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

Eswara

Ashique

Panchbhai

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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show abstract

“…However, authors usually abandon the temporal dynamics and historical experience of the user's satisfaction, which are referred to as the memory effects [8]. Some other studies attempt to clarify the role of primacy and recency effects [9][10][11][12][13][14][15], resulting in the high accurate QoE prediction. Typically, the primacy and recency effects [16] determine the memory influence of impairments occurring at the beginning and the end of streaming session [17], respectively.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

et al. 2019

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The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user's cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.

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Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

Venkatesh Naik,

Madhavi

2024

Concurrency and Computation

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SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

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Learning a Continuous-Time Streaming Video QoE Model

Cited by 59 publications

References 41 publications

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

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

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

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