A Quality-of-Experience Database for Adaptive Video Streaming

2018 ◽  
Vol 64 (2) ◽  
pp. 474-487 ◽  
Author(s):  
Zhengfang Duanmu ◽  
Abdul Rehman ◽  
Zhou Wang
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Adaptive Video Streaming ◽  
Adaptive Video

Towards Perceptually Optimized Adaptive Video Streaming -A Realistic Quality of Experience Database

2021 ◽  
pp. 1-1
Author(s):  
Christos G. Bampis ◽  
Zhi Li ◽  
Ioannis Katsavounidis ◽  
Te-Yuan Huang ◽  
Chaitanya Ekanadham ◽  
...  
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Adaptive Video Streaming ◽  
Adaptive Video

scholarly journals Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS

2018 ◽  
Vol 9 (3-4) ◽  
pp. 94 ◽  
Author(s):  
Miran Taha ◽  
Jose Miguel Jimenez ◽  
Alejandro Canovas ◽  
Jaime Lloret
Keyword(s):  
Video Streaming ◽  
Service Providers ◽  
Traditional Approach ◽  
Hybrid Architecture ◽  
Adaptive Video Streaming ◽  
Technology Standard ◽  
Adaptive Video ◽  
Objective Metrics ◽  
Buffer Length

Multimedia streaming is the most demanding and bandwidth hungry application in today’s world of Internet. MPEG-DASH as a video technology standard is designed for delivering live or on-demand streams in Internet to deliver best quality content with the fewest dropouts and least possible buffering. Hybrid architecture of DASH and eMBMS has attracted a great attention from the telecommunication industry and multimedia services. It is deployed in response to the immense demand in multimedia traffic. However, handover and limited available resources of the system affected on dropping segments of the adaptive video streaming in eMBMS and it creates an adverse impact on Quality of Experience (QoE), which is creating trouble for service providers and network providers towards delivering the service. In this paper, we derive a case study in eMBMS to approach to provide test measures evaluating MPEG-DASH QoE, by defining the metrics are influenced on QoE in eMBMS such as bandwidth and packet loss then we observe the objective metrics like stalling (number, duration and place), buffer length and accumulative video time. Moreover, we build a smart algorithm to predict rate of segments are lost in multicast adaptive video streaming. The algorithm deploys an estimation decision regards how to recover the lost segments. According to the obtained results based on our proposal algorithm, rate of lost segments is highly decreased by comparing to the traditional approach of MPEG-DASH multicast and unicast for high number of users.

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