This situation would unfortunately result in an incomplete viewing experience, causing viewers not enjoying the full entertainment value of the live program and therefore dissatisfaction with the quality of services. However, some of them could not do so and inevitably join in the middle of live program because of unexpected events. In general, people watching a live streaming program would sit and wait for its broadcasting at the very beginning. Being able to hold specific deadlines for up to 200 peers, predestines our solution to be suitable for the majority of 's channels. Evaluation shows the various impact of the number of peers, number of video parts and chunks on the streaming delay. In this paper, we present Chunked-Swarm, a swarm-based approach, which aims to offer predictable streaming delays, independently of the number of peers. Solutions up to now, mainly offer only best effort delay guarantees on the distribution speed from initial seeders to all peers. An alternative approach could enable peer-to-peer communication in order to utilize the capacities of the user devices. Due to latency, provides one or more servers for each of 's supported countries. Millions of viewers daily watch user channels, although roughly 85 % of all channels have less than 200 views during one session. Live user-generated video streaming platforms like generate a large portion of the Internet traffic. The snap-stabilization property of our proposed algorithm facilitates the reliability property by ensuring that content distribution proceeds as per its specification to reach all system peers without backtracking after starting in an arbitrary initial configuration. Due to being reliable, each multicast content is guaranteed to be received by all system peers exactly once regardless of the arbitrary initial system configuration in the presence of potentially n-1\documentclass other multicasts competing for network resources where n denotes the number of peers in the network. Concurrent mulicasts from different sources are synchronized so that multiple multicasts use each network channel mutually exclusively while ensuring that each multicast eventually succeeds to deliver its content to all system peers. The proposed algorithm synchronises concurrent multicasts in sharing network resources while providing the desirable properties of self- and snap-stabilization. In this paper, we propose the first novel reliable concurrent content multicast algorithm, referred to as a CD-wave algorithm, for concurrent distribution of content from some or all peers to all other network peers. Multicast is a one-to-many wave scheme for content distribution from a source to all or a large number of destinations. One of the approaches in reducing the content traffic and increasing the network throughput is the paradigm of multicast. Rapid growth in the number of users, devices and applications and infrastructures generating and demanding massive amount of content traffic on the Internet has necessitated the available network infrastructures to evolve to cope up with the ever increasing content traffic. The study aims to answer the following key questions: How peer-to-peer is BTLive? How delay optimized is BTLive? What is the overhead of BTLive? To answer these questions, traces of real BTLive traffic between a broadcast server and a number of peers deployed across Europe have been analyzed. To this end, this paper presents a measurement study of the official beta version of BTLive. So far, no publicly available study exists that quantitatively analyzes BTLive’s performance. For content providers investigating the applicability of BTLive’s approach, it is essential to understand its properties as well as its limitations. released a new P2P live streaming system termed BTLive, specifically targeted at low delay and low overhead. Various P2P streaming approaches have been proposed aiming at a good tradeoff between flexibility, streaming delay, and costs in terms of traffic overhead for both content providers and clients. Yet, P2P streaming typically comes at the cost of increased streaming delays caused by the inevitable multi-hop forwarding of content by peers within the overlay. The peer-to-peer approach can greatly help to cope with highly dynamic live streaming workload by using idle client resources.
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