A network-aware adaptive streaming for improving the video quality

2016 ◽  
Author(s):  
Dongchil Kim ◽  
Chang Mo Yang ◽  
Chai-Jong Song ◽  
Sungjoo Park
Keyword(s):  
Video Quality ◽  
Adaptive Streaming

Perceptual video quality assessment for adaptive streaming encoding

2015 ◽  
Author(s):  
Estevao C. Monteiro ◽  
Ricardo E. P. Scholz ◽  
Carlos A. G. Ferraz ◽  
Tsang I. Ren ◽  
Roberto S. M. Barros
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Video Quality Assessment ◽  
Adaptive Streaming ◽  
Perceptual Video Quality

scholarly journals A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hongyun Zheng ◽  
Yongxiang Zhao ◽  
Xi Lu ◽  
Rongzhen Cao
Keyword(s):  
Quality Of Experience ◽  
Fog Computing ◽  
Video Quality ◽  
Adaptive Streaming ◽  
Network Operator ◽  
Video Traffic ◽  
Client Server ◽  
Prediction Scheme ◽  
Network Equipment

Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

scholarly journals Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

2020 ◽  
Vol 10 (5) ◽  
pp. 1793
Author(s):  
Lina Du ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Jing Zhang ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Feature Vector ◽  
Video Quality ◽  
Superior Performance ◽  
Adaptive Streaming ◽  
Input Feature ◽  
Streaming Services ◽  
Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

A Video-Quality Controller for QoE Enhancement in HTTP Adaptive Streaming

2018 ◽  
Vol E101.B (4) ◽  
pp. 1163-1174
Author(s):  
Takumi KUROSAKA ◽  
Shungo MORI ◽  
Masaki BANDAI
Keyword(s):  
Video Quality ◽  
Adaptive Streaming ◽  
Http Adaptive Streaming ◽  
Quality Controller

A Model for a Practical Evaluation of a DASH-based Rate Adaptive Algorithm over HTTP.

2021 ◽  
Author(s):  
Muhammad Usman Younus ◽  
Rabia Shafi
Keyword(s):  
User Satisfaction ◽  
Video Quality ◽  
Optimal Solution ◽  
Modern Technology ◽  
Network Simulator ◽  
Adaptive Streaming ◽  
Adaptation Algorithm ◽  
Limited Bandwidth ◽  
Massive Growth ◽  
Media Presentation

Abstract The proliferation of multimedia devices and user-generated content has driven massive growth in Internet traffic for video streaming. There is a dire need to propose solutions that can pose challenging multimedia streaming issues to achieve a user’s quality. Dynamic adaptive streaming over HTTP (DASH) improves the user’s quality through practical systems with limited bandwidth that enables the streaming media to run smoothly. This modern technology can easily improve user perception and defines the media presentation description (MPD) in terms of URL, content file, etc. The proposed adaptation algorithm attempts to determine the optimal solution to alleviate the conflict between maximizing the video quality and avoiding buffer stalls. We evaluate the proposed algorithm against alternative solutions such as ALDASH and FDASH for video content by taking into account the video bitrate, buffer level, and video bitrate switches for the single-user environment. A set of experiments have been conducted to investigate and analyze the benefits of our proposed algorithm. A network simulator NS-3 is used to conduct the performance evaluation by our proposed algorithm. Furthermore, simulation results show that our proposed algorithm enhances video quality performance compared to ALDASH and FDASH in terms of user satisfaction. Last but not least, the experimental results of our proposed algorithm can provide high viewer quality in adaptive streaming as compared to ALDASH and FDASH.

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