In this paper, we propose a new Scalable Video Coding (SVC) quality-adaptive peer-to-peer television (P2PTV) system executed at the peers and at the network. The quality adaptation mechanisms are developed as follows: on one hand, the Layer Level Initialization (LLI) is used for adapting the video quality with the static resources at the peers in order to avoid long startup times. On the other hand, the Layer Level Adjustment (LLA) is invoked periodically to adjust the SVC layer to the fluctuation of the network conditions with the aim of predicting the possible stalls before their occurrence. Our results demonstrate that our mechanisms allow quickly adapting the video quality to various system changes while providing best Quality of Experience (QoE) that matches current resources of the peer devices and instantaneous throughput available at the network state.
In this paper, we propose a novel quality adaptation mechanisms using Scalable Video Coding (SVC) over Peer-toPeer Television (P2PTV) architecture in order to provide maximum quality level. Our algorithms are based on the idea of dividing quality adaptation into two stages. The first stage, Layer Level Initialization (LLI), allows adapting to static resources at the peers. After streaming starts, another set of algorithms called the Layer Level Adjustment (LLA) is used to adapt the H.264/SVC streams with available real time resources of P2PTV. LLA is evaluated and compared with Progressive Quality Adaptation (PQA) on Quality of PSNR metric using PSIM simulator. Performance evaluation demonstrates that LLA outperforms PQA and provides a significant improvement in the PSNR value in term of adapting the Quantization Parameter (QP) with instantaneous throughput available at the peer .
A new wavelet-based method is presented in this work for estimating and tracking the pitch period. The main idea of the proposed new approach consists in extracting the cepstrum excitation signal and applying on it a wavelet transform whose resulting approximation coefficients are smoothed, for a better pitch determination. Although the principle of the algorithms proposed has already been considered previously, the novelty of our methods relies in the use of powerful wavelet transforms well adapted to pitch determination. The wavelet transforms considered in this article are the discrete wavelet transform and the dual tree complex wavelet transform. This article, by all the provided experimental results, corroborates the idea of decomposing the cepstrum excitation by using wavelet transforms for improving pitch detection. Another interesting point of this article relies in using a simple but efficient voicing decision (which actually improves a similar voicing criterion we proposed in a preceding published study) which on one hand respects the real-time process with low latency and on the other hand allows obtaining low classifications errors. The accuracy of the proposed pitch tracking algorithms has been evaluated using the international Bagshaw and the Keele databases which include male and female speakers. Our various experimental results demonstrate that the proposed methods provide important performance improvements when compared with previously published pitch determination algorithms.
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