Quality of Experience Study on Dynamic Adaptive Streaming Based on HTTP

2015 ◽  
Vol E98.B (1) ◽  
pp. 62-70 ◽  
Author(s):  
Yun SHEN ◽  
Yitong LIU ◽  
Hongwen YANG ◽  
Dacheng YANG
Keyword(s):  
Quality Of Experience ◽  
Adaptive Streaming

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.

scholarly journals HAS Based Empirical QoE Study over TCP and QUIC on Diverse Networks

2020 ◽  
Author(s):  
Md Islam ◽  
Christian Rothenberg
Keyword(s):  
Quality Of Experience ◽  
Transport Protocol ◽  
High Fidelity ◽  
Behavioral Performance ◽  
Adaptive Streaming ◽  
Abs Algorithms ◽  
Http Adaptive Streaming ◽  
Network Status ◽  
Reliable Transport

HTTP adaptive streaming (HAS) is the de-facto standard for video services over the Internet delivering increased Quality of Experience (QoE) as a function of the network status. Such adaptive streaming atop HTTP relies predominantly on TCP as the reliable transport protocol. Recently, QUIC, an alternative of TCP transport, has emerged to overcome TCP’s native shortcomings and improve the HTTP-based applications QoE. This paper investigates three strategies (Rate, Buffer, and Hybrid) based adaptive bitrate streaming (ABS) algorithms behavioral performance over the traditional TCP and QUIC transport protocol. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the performance of ABS algorithms considering QoE metrics over TCP and QUIC. Our empirical results show that each ABS algorithm’s (Conventional, BBA, and Arbiter) QoE performance is biased for TCP. As a result, QUIC suffers the ineffectiveness of traditional state-of-art ABS algorithms to improve video streaming performance without specific changes.

scholarly journals Adaptive Streaming of Scalable Videos over P2PTV

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Youssef Lahbabi ◽  
El Hassan Ibn Elhaj ◽  
Ahmed Hammouch
Keyword(s):  
Video Coding ◽  
Quality Of Experience ◽  
Scalable Video Coding ◽  
Video Quality ◽  
The Other ◽  
Scalable Video ◽  
Adaptive Streaming ◽  
Network State ◽  
Adaptation Mechanisms

In this paper, we propose a new Scalable Video Coding (SVC) quality-adaptive peer-to-peer television (P2PTV) system executed at the peers and at the network. The quality adaptation mechanisms are developed as follows: on one hand, the Layer Level Initialization (LLI) is used for adapting the video quality with the static resources at the peers in order to avoid long startup times. On the other hand, the Layer Level Adjustment (LLA) is invoked periodically to adjust the SVC layer to the fluctuation of the network conditions with the aim of predicting the possible stalls before their occurrence. Our results demonstrate that our mechanisms allow quickly adapting the video quality to various system changes while providing best Quality of Experience (QoE) that matches current resources of the peer devices and instantaneous throughput available at the network state.

Quality of Experience for HTTP Adaptive Streaming Services

2015 ◽  
pp. 69-98
Author(s):  
Ozgur Oyman ◽  
Vishwanath Ramamurthi ◽  
Utsaw Kumar ◽  
Mohamed Rehan ◽  
Rana Morsi
Keyword(s):  
Quality Of Experience ◽  
Adaptive Streaming ◽  
Http Adaptive Streaming ◽  
Streaming Services
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