Cell-Cluster Network-Assisted Adaptive Streaming Media optimization over Wireless Network

Author(s):  
Yubo Shen ◽  
Yitong Liu ◽  
Hongwen Yang ◽  
Lin Sang ◽  
Wei He
2014 ◽  
Vol 701-702 ◽  
pp. 943-946
Author(s):  
Nian Fang Hong

In the wireless network environment, a large number of applications based on cell phone have emerged. But it has appeared some problems such as large amount of data and limited bandwidth and higher quality transmission in the mobile streaming media data transmission. To solve these problems, this paper designs a bandwidth adaptive streaming media real-time synchronization algorithm. Algorithm firstly analysis the state of the network, and then through real-time increase or decrease the factor method to effectively adjust the code flow rate, thus improve the QoS of streaming in transmission; to meet the learners' online learning, for subsequent teaching and interaction provides a good technical support.


2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yepeng Ni ◽  
Qianjun Shuai ◽  
Cheng Yang ◽  
Jianbo Liu

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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