P2P VoD System Research Articles

QoE Oriented Chunk Scheduling in P2P-VoD Streaming System

Peer-to-peer-based video on demand (P2P-VoD) streaming systems have been widely used in delivering multimedia content. However, There are several problems existing in the P2P-VoD streaming systems, such as unstable video playing, low-utilization rate of video, and high server load, severely influencing viewers’ quality-of-experience (QoE). How to reasonably and efficiently schedule video chunks is important to handle the above-mentioned issues. In this paper, we propose a novel QoE-oriented chunk scheduling in a P2P-VoD streaming system, containing a request queue-based load balancing (RQLB) strategy and an elimination index-based cache replacement (EICR) strategy. In the proposed RQLB strategy, we first define the priority of the chunk requests, making the high-priority requests handled in time. Subsequently, we construct the utilization function of peer, so that the relatively low-priority requests will be transferred to the candidate neighbors with low utilization and high reliability. In the designed EICR strategy, we first calculate the elimination index for determining the candidate video. Considering the fact that chunks in a video may have different popularities, we then select the chunk with the lowest cache value for replacement. Moreover, we implement a real P2P-VoD system based on the proposed chunk scheduling scheme, giving the detailed design process of the network elements. Experimental results show that our proposed strategies can effectively solve chunk scheduling problem in the P2P-VoD streaming system, guaranteeing viewers’ QoE.

A new mechanism to improve video streaming in P2P networks using helper nodes

Peer-to-peer video on demand (P2P-VoD) is a solution to provide video service to a large number of users on the Internet. Due to the upload bandwidth bottleneck of VoD servers, the streaming capacity in the P2P-VoD system is limited. This paper proposes a new mechanism to improve the video streaming in P2P networks by using the idle peers upload bandwidth and storage, which are called helpers. The proposed method uses an XOR mechanism in the VoD server to encode chunks and reduce bandwidth use. The helpers receive encoded chunks from the VoD server and save them in their storage spaces and cooperate in sharing these chunks with the other peers. Simulation results using OMNET++ are provided to show the efficiency of the proposed mechanism in terms of the required upload capacity requirement of the server and deploying the storage and bandwidth of helpers. The results of simulation show that the upload bandwidth usage of the server has been reduced. On the other hand, the proposed method increases the memory usage of helpers.

Tackling energy and battery issues in mobile P2P VoD systems

As video streaming services dominate the global internet traffic nowadays, several service providers are considering a shift towards peer-to-peer (P2P) architectures in order to lower their costs. However, these architectures tend to use the peers’ network resources aggressively without taking any energy-efficient measures. Ignoring battery constraints in a P2P service that is dominated by mobile users can significantly deteriorate the system’s efficiency and lead to low Quality of Experience (QoE) to all users. In this paper, we examine the effect of battery and energy consumption in the performance of P2P VoD systems and we present a novel distributed system that successfully utilizes peers’ upload bandwidth resources, while taking into account energy and battery resources. Its components that manage the overlay and the data scheduling are tested under extensive simulations that show promising results.

Dynamic Strategy Selection based on Evidence Theory. Application on a P2P-VoD system

Peer-to-Peer Video-on-Demand (VoD) systems are rising as a new dominant way to distribute video content over IP networks. While many works addressed the broad area of P2P communications, few work has been focused onto the resource allocation in P2P streaming systems, where the real-time constraint adds another dimension to the problem. Most work on P2P resource allocation solve the problem with static rules strategies are not robust when they are facing to the content demand (popularity) trends. In this paper, we use a dynamic approach, which automatically selects the best strategy on a given time period. With the proposed P2P resource allocation method, a VoD service provider can combine any number of resource allocation strategies and formulate different performance objectives that meet its requirements. As part of our effort to develop algorithms that can be effectively deployed by broadband operators, we developed a full-scale emulator for P2P streaming that can support up to 10,000 peers. The P2P streaming emulator includes a VoD request generators, along with a SuperNode (with backend DataBase) that tracks and allocates network resources. In this paper the goal is to use a low complexity learning approach to enhance the performance of a P2P-VoD system based on evidence theory. The proposed approach is compared to both static and dynamic solutions. The obtained results show that the proposed approach allows to reduce the complexity of selecting resource allocation strategies, while maintaining a low rejection rate.

A Proposed Peer Selection Algorithm for Transmission Scheduling in P2P-VOD Systems

Video transmission in peer-to-peer video-on-demand faces some challenges. These challenges include long transmission delay and poor quality of service. The peer selection plays an important role in enhancing transmission efficiency. For this reason, a proposed algorithm for peer selection is introduced to overcome these challenges. The proposed algorithm consists of four steps. First, the peers exchange their own buffer maps with other peers. Second, the requested segments are ordered according to their priorities. Third, neighbors of the receiver are evaluated by the efficiency estimation. Finally, the efficient sender list is applied to solve the overloading and bottleneck on the highest efficient sender. A simulation is introduced to evaluate the performance of the proposed algorithm compared to a peer selection algorithm with context-aware adaptive (CAA) data scheduling algorithm. The results show that, the proposed algorithm reduces initial buffering delay and achieves high throughput rather than CAA algorithm.

Design of a Manageable P2P VoD System

For the lack of management mechanism in P2P VoD system, this paper presents design of a manageable P2P VoD system, describes the system architecture and design of each module, and been tested on CERNET2. By using intelligence node management, it has resolved the inefficient management problem of traditional P2P distribution system.

SBDP: Bandwidth prediction mechanism towards server demands in P2P-VoD system

Migrating popular videos into cloud is an efficient mechanism to decrease the costs of VoD services, but how to get the set of popular videos is a key problem. In this paper, we propose a prediction mechanism named SBDP to evaluate future server bandwidth demands on each video. Firstly, SBDP adopts time-series analysis techniques and history dataset to predict online population in advance. Then in order to obtain initial online population, the curve shape similar (CSS) and Gaussian process regression (GPR) methods are introduced. And finally a multi linear regression (MLR) method with the online population as a main factor is proved and applied in predicting seeds’ upload bandwidth. The proposed methods are simulated on large dataset collected from a popular VoD services in China and the simulation results demonstrate that the prediction data are close to real-world data.

Model Research of p2p VoD System based on Fluency

In the peer to peer (p2p) VoD system, there are many factors to influence the fluency of video, such as watching behavior, distribution of segments, download rates, data transmission, and so on. Most research concentrates on single factor without considering the interrelation of all the factors. In the paper, factors would be researched separately to find the relation with fluency of video playing. The mechanism can improve the download ratio with condition of keep watching smoothly. The model proposes a precision method to understand users’ watching behavior and can help to optimize design in other kinds of p2p application.

Exploring the policy selection of the P2P VoD system: A simulation-based research

The P2P-assisted video-on-demand (P2P VoD) service has achieved tremendous success among the Internet users. There are three core strategies in the P2P VoD system: the piece selection policy, the peer selection policy as well as the replica management policy. Different from the existing research works that only consider single policy optimization, we for the first time study the existing P2P VoD policies by using a simulation framework to understand the performance of different policy compositions. The simulation results indicate that when the bandwidth and storage resources are limited in the P2P VoD system, the composition of the sequential piece selection policy, the cascading peer selection policy and the proportional replica management policy has the best performance among all different policy compositions. However, when the bandwidth and storage resources are sufficient in the P2P VoD system, there will be little difference between different choices.

Distributed Caching via Rewarding: An Incentive Scheme Design in P2P-VoD Systems

Peer-to-peer (P2P) systems rely on peers' cooperation to provide a more robust and scalable service as compared to the traditional client-server architecture. However, the peers might be selfish in nature-they would like to receive services from others, but would not like to contribute their own resources by default. To conquer this problem, proper incentive schemes are needed so as to stimulate the peers' contributions. In particular, in P2P video-on-demand (VoD) systems, peers need to distributively cache the proper videos so as to mutually upload and help each other to acquire the required data. Content providers of P2P-VoD services want to incentivize peers to do so and alleviate the workload of the content server. In this paper, we design a practical mechanism to incentivize distributed caching in such systems, under which the peers are rewarded based on the popularity of the video they cache. We characterize the impact of this incentive scheme on peers' caching behaviors. In particular, we formulate an optimization framework to decide the optimal reward price for each video so as to keep enough replicas and minimize the content provider's operational cost. We first derive close form solutions in an asymptotic system, and then extend our results to be adaptive to various practical issues. Via extensive simulations, we validate the effectiveness and efficiency of our incentive scheme.

Modeling analysis of server loading in large-scale P2P-VoD systems with actively collaborative helpers

Through moving a significant fraction of uploading from the servers to the peers, using P2P approach can effectively alleviate server loading in VoD deployments. However, users' asynchronism dilutes their ability to assist each other. We develop a theoretical model to conduct an in-depth study of average server loading in large-scale P2P-VoD systems. This model captures many of the essential features of average server loading in large-scale P2P-VoD systems. Based on the in-depth study on average server loading, we introduce actively collaborative helpers into systems for alleviating server loading. The model of peers is further developed to a hybrid model for studying the optimal download amount of helpers from perspective of the entire network including peers and helpers. We show there exactly exists the sweet point of download amount of homogeneous helpers. With downloading the sweet amount, helpers can contribute the bandwidth around twice than they consume, when they do not assist each other, and even approximately 4 times when they assist each other.

Bandwidth-Availability-Based Replication Strategy for P2P VoD Systems

Bandwidth-Availability-Based Replication Strategy for P2P VoD Systems

Modeling the effect of user interactions on mesh-based P2P VoD streaming systems

User interactions such as seeks and pauses are widely supported by existing Peer-to-Peer Video-on-Demand (P2P VoD) streaming systems. Their effect on the streaming system, however, has not been well studied. Seeks cause peers to skip part of the video, making them stay in the system for shorter time, and thus contribute less. On the other hand, only part of the video is downloaded due to seeks, reducing peers' demand from the system. It is unclear which factor dominates the effect of seeks on the streaming system. Pauses during playback, on one hand, allow peers to stay longer in the system and upload more content. When interleaved with seeks, however, long pauses may increase peers' demand unnecessarily as peers may download content that will eventually be skipped by subsequent forward seeks. The collective effect of seeks and pauses, together with the known random peer departure, is unintuitive and needs to be addressed properly so as to understand the effect of human factors on the streaming system performance. In this article, we develop an analytical model to both qualitatively and quantitatively study the effect of seeks and pauses on mesh-based P2P VoD streaming systems, in particular, the effect on the server cost. Our model can help in understanding how human factors such as seeks and pauses affect the streaming system performance, tuning a P2P VoD system towards better system performance and stability, and providing a framework for capacity planning.

On incentivizing upload capacity in P2P-VoD systems: Design, analysis and evaluation

Free riding has long been a serious problem in peer-to-peer (P2P) systems due to the selfish behavior of individual users. To conquer this problem, a key design issue of the P2P systems is to appropriately incentivize users to contribute resources. In P2P Video-on-Demand (VoD) applications, content providers need to incentivize the peers to dedicate bandwidth and upload data to one other so as to alleviate the upload workload of their content servers. In this paper, we design a simple yet practical incentive mechanism that rewards each peer based on its dedicated upload bandwidth. We use a mean field interaction model to characterize the distribution of number of peers in different video segments, based on which we characterize the content providers’ uploading cost as a function of the peers’ contribution. By using a game theoretic framework, we analyze the interaction between a content provider’s rewarding strategy and the peers’ contributing behaviors and derive a unique Stackelberg equilibrium. We further analyze the system efficiency in terms of the price of anarchy and study the long term behavior of the system under a repeated game setting. Via extensive simulations, we validate the stability and efficiency of our incentive scheme.

An efficient server bandwidth costs decreased mechanism towards mobile devices in cloud-assisted P2P-VoD system

Recently, more and more devices with small buffer size such as PDAs or mobile phones are joining in the VoD system, which leads to two major challenges: how to efficiently distribute their bandwidth resources with small buffer size, and how to provide assistant mechanism to make them playback smoothness. In face of this situation and for the purpose of decreasing the server bandwidth costs, we propose a peers’ downloading mechanism called NCDLT to solve above challenges. It contains two algorithms. The first is neighbors and chunks downloading selection (NCS) algorithm and it ensures peers to find neighbors who can provide video data with lower refusal rate. The second is distributed linear taxation algorithm (DLT) and it makes peers with lower capability acquire enough download rate to reduce the request to servers. The simulation results demonstrate that our algorithms can offload the server bandwidth costs and improve the download rate of peers with small buffer size.

GMaker: A video recommendation module for peer-assisted VoD

Peer-assisted Video-on-Demand (VoD) systems allow users to watch different parts of a video at the same time, which leads to an inefficient video content sharing among the users. Therefore, in P2P-VoD, it is difficult to alleviate the server load and meanwhile maintain a smooth playback. Nevertheless, if the population of a swarm is sufficiently large and under the server’s capacity, the efficiency can be improved to some extent. We, therefore, introduce the concept of Groupon into P2P-VoD system. Groupon is the most successful web site that uses the business model called deal-of-the-day that can attract a massive group of people and thus benefit both the customers and the retailers. Thus, a Groupon-like module named GMaker is proposed in P2P-VoD, which recommends the most suitable video to the user. In this case, a moderate number of audiences are gathered in one overlay. As a result, the upload capacity of the user is sufficiently used and the workload of media server can be alleviated. We support our design with extensive simulations to compare the performance of P2P-VoD applying GMaker and the one not applying GMaker in different scenarios. It shows that GMaker significantly enhances the performance of P2P-VoD resource utilization, quality of service of users and video support capacity.

Popularity Aware Limited Caching for Reliable On Demand P2P Video Streaming

In nowadays network group, the Peer-To-Peer (P2P) network is exploring as a good environment for resource sharing over the Internet. Compared with traditional file sharing workloads, continuous streaming of multimedia content provokes a significant amount of today’s internet traffic. Streaming media has various real-time constraints such as insufficient memory, high bandwidth utilization for largescale media objects, and lack of cooperation between proxies and their clients. Under these circumstances, it is a challenging task to achieve efficient content delivery under the increased availability of continuous-media streaming. In this paper, PALC (Popularity Aware Limited caching) is proposed to achieve efficient, reliable and scalable P2P-VoD system. It is an attractive approach to minimize the network traffic incurred in video streaming through cooperative caching. In this approach, P2P network is constructed in the form of a two-tier architecture, where the upper tier is composed of proxy overlay with high connectivity super peers. It serves as a structured DHT node to maintenance the detached hash table for prominent information retrieval. On the contrary, in lower tier, less connectivity peers are loosely coupled to the corresponding super peers in the form of unstructured network. We proposed an efficient hybrid proxy caching system in order to reduce the network/ server load while improving the end user perceived video quality. It also proposes an efficient Popularity Aware Limited Caching (PALC) algorithm that cooperatively utilizes the memory space of the proxy servers and clients to achieve efficient and cost effective P2P video streaming. The effectiveness of the proposed approach is evaluated under various network and end system configurations. The experimental results demonstrate that the proposed approach incurs minimum Internet traffic and low cost of transmission as compared to the proxy-based caching approach with limited storage space.

Exploring the Optimal Replication Strategy in P2P-VoD Systems: Characterization and Evaluation

P2P-Video-on-Demand (P2P-VoD) is a popular Internet service which aims to provide a scalable and high-quality service to users. At the same time, content providers of P2P-VoD services also need to make sure that the service is operated with a manageable operating cost. Given the volume-based charging model by ISPs, P2P-VoD content providers would like to reduce peers' access to the content server so as to reduce the operating cost. In this paper, we address an important open problem: what is the “optimal replication ratio” in a P2P-VoD system such that peers will receive service from each other and at the same time, reduce the access to the content server? We address two fundamental issues: 1) what is the optimal replication ratio of a movie if we know its popularity, and 2) how to achieve these optimal ratios in a distributed and dynamic fashion. We first formally show how movie popularities can impact server's workload, and formulate the video replication as an optimization problem. We show that the conventional wisdom of using the proportional replication strategy is “suboptimal,” and expand the design space to both “passive replacement policy” and “active push policy” to achieve the optimal replication ratios. We consider practical implementation issues, evaluate the performance of P2P-VoD systems and show how to greatly reduce server's workload and improve streaming quality via our distributed algorithms.

Query Adaptation Techniques in Temporal-DHT for P2P Media Streaming Applications

Peer-to-Peer (P2P)-based approach for on-demand video streaming systems (P2P-VoD) characterized by asynchronous user-interactivity has proven to be practical and effective in recent years with real-world Internet-scale deployment (Huang, Li, & Ross, 2007). Current state-of-art P2P-VoD systems employ tracker server for discovering content suppliers which poses scalability and bottleneck issues. Temporal-DHT is a structured P2P based approach which can efficiently accommodate the large number of update operations with the continuous change of user’s playing position and supporting asynchronous jumps (Bhattacharya, Yang, & Zhang, 2010). The authors propose different query adaptation strategies based upon content popularity distributions and shortage bandwidth ratios which are proved to be effective in improving the performance of P2P streaming system by deriving certain optimized solutions. They formulate valuable optimization problems in the context of a P2P-VoD system such as minimization of query search cost, server bandwidth consumption, and a joint cost-load framework. The authors provide optimized solutions that achieve the best result for the above mentioned optimization objectives. They show extensive simulation studies under various scenarios of search cost, streaming quality, and other associated factors in a dynamic network environment where users are free to asynchronously join/leave the system.

Server-assisted adaptive video replication for P2P VoD

In recent years, Peer-to-Peer assisted Video-on-Demand (P2P VoD) has become an effective and efficient approach to distribute high-quality videos to large number of peers. In a P2P VoD system, each peer contributes storage to store several videos to help offload the server. The replication strategy, which determines the videos to be stored at each peer's local storage, plays an important role in system performance. There are two approaches: (a) solve a huge combinatorial optimization problem and (b) use simple cache replacement algorithms, such as Least-Frequently-Requested (LFR) or FIFO. The first approach needs to collect a large number of parameters whose values may be changing, and use some approximation method (such as linearization) to solve the optimization problem, both aspects have accuracy issues. In the second approach, a peer replaces some video in the cache with the currently viewed video, based on local information. While it is simple, we show their performance can be improved by a little centrally collected state information. Specifically, the needed feedback information is the current downloading rate provided by peers for each video. In this paper, we describe a hybrid replication strategy, and give detailed description of how the server collects and maintains the feedback information, and how peers use that information to determine what videos to store and indirectly control their uplink bandwidth contribution. This explains why the hybrid strategy is much simpler and more practical than the combinatory optimization approach. We then use simulation to demonstrate how our scheme out-performs the simple adaptive algorithms. Our simulation results also demonstrate how our scheme is able to quickly respond to peer churn and video popularity churn.