Robust vote sampling in a P2P media distribution system

2009 ◽  
Author(s):  
Rameez Rahman ◽  
David Hales ◽  
Michel Meulpolder ◽  
Vincent Heinink ◽  
Johan Pouwelse ◽  
...  
Keyword(s):  
Distribution System ◽  
Media Distribution

MSPnet: Manageable SIP P2P media distribution system

2007 ◽  
Vol 24 (6) ◽  
pp. 845-853
Author(s):  
Jie He ◽  
Yongfeng Huang ◽  
Xing Li
Keyword(s):  
Distribution System ◽  
Media Distribution

scholarly journals A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yepeng Ni ◽  
Qianjun Shuai ◽  
Cheng Yang ◽  
Jianbo Liu
Keyword(s):  
Distribution System ◽  
Heterogeneous Network ◽  
Streaming Media ◽  
Service Level ◽  
Prototype System ◽  
Context Aware ◽  
Adaptive Streaming ◽  
Network State ◽  
Media Distribution ◽  
Environment Parameters

We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR).

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