Download Peer-to-Peer Video Streaming by Eric Setton PDF

By Eric Setton

Peer-to-Peer Video Streaming describes novel options to reinforce video caliber, elevate robustness to mistakes, and decrease end-to-end latency in video streaming platforms. This publication could be of use to either teachers and pros because it provides thorough assurance and options for present concerns with Video Streaming and Peer-to-Peer architectures. The ebook offers an summary of today’s state-of-the paintings video streaming expertise. It offers adaptive video coding and streaming strategies for functionality enhancement of traditional client-server platforms and P2P multicast. The particular appendix contains quite a few extra experiments.

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Peer-to-Peer Video Streaming

Peer-to-Peer Video Streaming describes novel recommendations to reinforce video caliber, raise robustness to blunders, and decrease end-to-end latency in video streaming platforms. This publication might be of use to either lecturers and execs because it provides thorough insurance and ideas for present matters with Video Streaming and Peer-to-Peer architectures.

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Extra resources for Peer-to-Peer Video Streaming

Example text

CoDiO vs. RaDiO We compare the performance of CoDiO vs. RaDiO in terms of distortion, rate and congestion in Fig. 19. For RaDiO, we follow exactly the procedure used for CoDiO except that we evaluate different schedules in terms of their expected Lagrangian cost D(π) + λR(π). Values for the parameter λ are determined experimentally for each of the 6 sequences, in the same way as they were for CoDiO. They are indicated in Tab. 2. The value of R is obtained by computing the average transmitted rate over the time horizon considered.

Specifically, this model expresses that at lower rates, reconstructed video quality is limited by coarse quantization, whereas at higher rates, the video stream will lead to more network congestion and to longer packet delays, which in turn will cause higher probability of late packets, hence reduced quality. For low-latency video steaming in bandwidth-limited environments, we therefore expect to achieve maximum decoded quality for some intermediate rate. 9) with respect to R. This reduces to: ΘL e−(C κT (1 − Pr ) −R∗ )T /2L − R∗ + R0 = 0.

Linear regression to determine the value of the parameter κ for 6 sequences. The encoding rate for the different sequences is as follows. Container: 283 kb/s, Foreman: 290 kb/s, Mobile: 306 kb/s, Mother and Daughter: 319 kb/s, News: 319 kb/s, Salesman: 311 kb/s. The estimated value of κ for the different sequences is as follows. Container: 143, Foreman: 580, Mobile: 2262, Mother and Daughter: 64, News: 180, Salesman: 130. For low-latency streaming, Ploss reflects the combination of random losses and late arrivals of video packets.

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