Scalable video traffic offloading for streaming services in 5G HetNets

2021 ◽  
Author(s):  
Majid Abiri ◽  
Mehri Mehrjoo ◽  
Mehdi Rezaei
Keyword(s):  
Scalable Video ◽  
Video Traffic ◽  
Traffic Offloading ◽  
Streaming Services

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

scholarly journals Analysis and Modeling of H.264 Unconstrained VBR Video Traffic

2009 ◽  
Vol 1 (4) ◽  
pp. 14-31 ◽  
Author(s):  
Harilaos Koumaras ◽  
Charalampos Skianis ◽  
Anastasios Kourtis
Keyword(s):  
Video Coding ◽  
Communication Networks ◽  
Traffic Control ◽  
Expert Group ◽  
Video Traffic ◽  
Network Resources ◽  
Frame Size ◽  
Streaming Services ◽  
Vbr Video

In future communication networks, video is expected to represent a large portion of the total traffic, given thatespecially variable bit rate (VBR) coded video streams, are becoming increasingly popular. Consequently, traffic modeling and characterization of such video services is essential for the efficient traffic control and resource management. Besides, providing an insight of video coding mechanisms, traffic models can be used as a tool for the allocation of network resources, the design of efficient networks for streaming services and the reassurance of specific QoS characteristics to the end users. The new H.264/AVC standard, proposed by the ITU-T Video Coding Expert Group (VCEG) and ISO/IEC Moving Pictures Expert Group (MPEG), is expected to dominate in upcoming multimedia services, due to the fact that it outperforms in many fields the previous encoded standards. This article presents both a frame and a layer (i.e. I, P and B frames) level analysis of H.264 encoded sources. Analysis of the data suggests that the video traffic can be considered as a stationary stochastic process with an autocorrelation function of exponentially fast decay and a marginal frame size distribution of approximately Gamma form. Finally, based on the statistical analysis, an efficient model of H.264 video traffic is proposed.

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