Yung-Chang Chen

This paper proposes a sender-driven peer selection scheme, including estimation of packet loss propagation, evaluation of peers' contributions, and peer selection based on child-peers' contributions, for mesh-based peer-to-peer (P2P) video streaming systems. The proposed packet loss propagation model takes into account the link packet drop rate, peer dynamics, and forward error correction protection to capture the heterogeneous packet loss behavior of individual substreams transmitted over a mesh network. The evaluation of candidate peers' contributions is modeled through Markov random fields to significantly reduce complexity. Simulation results demonstrate that our peer selection scheme significantly mitigates packet loss in a mesh-based P2P network, compared to other state-of-the-art schemes.

Video transport over wireless networks requires retransmissions to successfully deliver video data to a receiver in case of packet loss, leading to increased delay time for the data to arrive at the receiver. Delay constraint is one of the most important requirements in real-time applications. A video packet arriving later than the presentation time will become useless for the client. In this paper, we propose a cross-layer content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs. Video packets of different importance are unequally protected with different retry limits at the media access control (MAC) layer. The error propagation effect of each packet is estimated to guide the determination of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Our scheme also analyzes the backoff time for each retry and then takes into account the estimated backoff time for retransmission scheduling. Experimental results show that the proposed adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, so as to improve video quality significantly.

The performance of a face recognition system degrades incredibly due to the variation of facial appearance with different pose, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual face. The ideal solution is to reconstruct a 3D model from the input images and synthesize the virtual image with corresponding pose, which might be too complex to be implemented in a real-time application. By formulating this kind of solutions as a nonlinear pose normalization problem, we will propose an algorithm integrating the nonlinearity of kernel function and the efficiency of linear regression method, which modifies the linear assumption in local linear regression (LLR) method and makes the solution more resembling to the ideal one. Some discussions and experiments on CMU PIE database are carried out, and show that our proposed method performs well.

In this paper, we propose a content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs (WLANs). Video packets of different importance are unequally protected with different retry limits at the MAC layer. The loss impact of each packet is estimated to guide the selection of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Our scheme also analyzes the backoff time for each retry and then takes into account the estimated backoff time for retransmission scheduling. Experimental results show that our adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, thereby improving video quality significantly

In this paper, we propose a content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs (WLANs). Video packets of different importance are unequally protected with different retry limits at the MAC layer. The loss impact of each packet is estimated to guide the selection of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Experimental results show that the proposed adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, thereby improving video quality significantly