Improving Streaming Capacity In Tree & Meshed Based P2P Live Streaming Systems

The streaming capacity for a channel is defined as the maximum streaming rate that can be achieved by every user in the channel. In the thesis, we investigated the streaming capacity problem in both tree-based and mesh-based Peer-to-Peer (P2P) live streaming systems, respectively. In tree-based multi-channel P2P live streaming systems, we propose a crosschannel resource sharing approach to improve the streaming capacity. We use cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we propose a resource sharing approach to improve the streaming capacity. In mesh-based P2P streaming systems, each peer exchanges video chunks with a set of its neighbors. We formulate the streaming capacity problem into an optimal resource allocation problem. By solving the optimization problem, we can optimally allocate the link rates for each peer, thus improve the streaming capacity.

Improving Streaming Capacity In Tree & Meshed Based P2P Live Streaming Systems

The streaming capacity for a channel is defined as the maximum streaming rate that can be achieved by every user in the channel. In the thesis, we investigated the streaming capacity problem in both tree-based and mesh-based Peer-to-Peer (P2P) live streaming systems, respectively. In tree-based multi-channel P2P live streaming systems, we propose a crosschannel resource sharing approach to improve the streaming capacity. We use cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we propose a resource sharing approach to improve the streaming capacity. In mesh-based P2P streaming systems, each peer exchanges video chunks with a set of its neighbors. We formulate the streaming capacity problem into an optimal resource allocation problem. By solving the optimization problem, we can optimally allocate the link rates for each peer, thus improve the streaming capacity.

REAL-TIME INTERACTIVE MECHANISM FOR LIVE STREAMING ON PEER-TO-PEER NETWORKS

Quality of live video streaming technology is based on quality of Experiences parameters (QoE). Approaching the peer-to-peer (P2P) or peer-assisted networks as a sympathetic solution is highly required, especially in light of its authentic scalability and its extremely low initial cost requirements. However, the design of robust, efficient, and performing P2P streaming systems remains a high challenge when real-time constraints are part of the quality of service (QoS), as in TV distribution or conferencing applications. One of the P2P main issues that affect the quality of streaming is the neighbor selection methodology. The proposed work presents an effective mesh-based neighbor selection approaches for video streaming – Uniform Peer Distribution Algorithm (UPDA) – based on QoS and QoE Parameters. UPDA shortens the latency to be ranging from 10 ms to 50 ms servicing up to 4000 online peers under failure / recovery tests. Simulation results demonstrate that the proposed UPDA achieves good performance in End-to End delay with a percentage of 10.4 % and packet delay variation about 2% compared to random neighbor selection method.

Enrich multi-channel P2P VoD streaming based on dynamic replication strategy

Peer-to-Peer Video-on-Demand (VoD) is a favorable solution which compromises thousands of videos to millions of users with completeinteractive video watching stream. Most of the profitable P2P streaming groupsPPLive, PPStream and UUSee have announced a multi-channel P2P VoD system that approvals user to view extra one channel at a time. The present multiple channel P2P VoD system resonant a video at a low streaming rate due to the channel resource inequity and channel churn. In order to growth the streaming capacity, this paper highlights completely different effective helpers created resource balancing scheme that actively recognizes the supply-and-demand inequity in multiple channels. Moreover, peers in an extra channel help its unused bandwidth resources to peers in a shortage channel that minimizes the server bandwidth consumption. To provide a desired replication ratio for optimal caching, it develops a dynamic replication strategy that optimally tunes the number of replicas based on dynamic popularity in a distributed and dynamic routine. This work accurately forecasts the varying popularity over time using Auto-Regressive Integrated Moving Average (ARIMA) model, an effective time-series forecasting technique that supports dynamic environment. Experimental assessment displays that the offered dynamic replication strategy which should achieves high streaming capacity under reduced server workload when associated to existing replication algorithms.