Abstract
Current video streaming services use a conventional, client-server network topology that puts a heavy load on content servers. Previous work has shown that Peer-to-Peer (P2P) assisted streaming solutions can potentially reduce this load. However, implementing P2P-assisted streaming poses several challenges in modern networks. Users tend to stream videos on the go, using their mobile devices. This mobility makes the network difficult to orchestrate. Furthermore, peers have to contribute their storage to the network, which is challenging, since mobile devices have limited resources compared to desktop machines. In this paper, we introduce an analytical framework for mobile P2P-assisted streaming to estimate the server load that we define as the minimum required server upload rate. Using our framework, we evaluate four caching strategies: infinite cache as a baseline, first in first out (FIFO), random, and Random Linear Network Coded (RLNC) cache. We verify our analytical results with empirical data that was obtained by carrying out extensive measurements on our working P2P system. Our results show that when employing FIFO, random, and RLNC caching strategies, the server load converges to that of the infinite cache as the cache size increases. With a limit of 5 P2P connections per peer, we show that using the random caching, peers can store 40% fewer packets and still achieve the same benefit as with FIFO caching. When using the RLNC caching, it is enough to store 50% fewer packets to achieve the same benefit.
Highlights
Peer-to-Peer (P2P) assisted streaming systems are comprised of a single server and multiple peers
4) Our results show that random caching outperforms first in first out (FIFO) caching in terms of server load, while Random Linear Network Coding (RLNC) encoded caching reaches the theoretical optimal, while only caching 20% of the original content
Since peers only download from the server if they cannot collect a packet from the network, any time the server is overloaded, the Quality of Experience (QoE) is affected
Summary
Peer-to-Peer (P2P) assisted streaming systems are comprised of a single server and multiple peers. The set of nearby available peers is continuously changing, which makes connection planning challenging and centralized connection orchestration unfeasible These challenges, characteristic of the mobile environment, make it difficult for the content providers to approximate the required server upload rate of a given P2P-assisted service. Wu et al presented mathematical models and formulated an optimization framework to understand the impact of movies’ popularities on servers’ workload [11] They proposed a passive and active video replication strategy, where data is passively deleted when the peers’ storage is full, while. Fujita investigated P2P-assisted delivery networks with multiple trees as the underlying topology of the overlay network [14] He focused on 2-hop content delivery solutions where the video stream is divided into α stripes, and there are n peers in the network. The peers aim to gather all L packets in a streaming way (i.e., downloading them in sequential order)
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