Hybrid ants-like search algorithms for P2P media streaming distribution in ad hoc networks

Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions,po-tential high loss rate and the decentralized architecture. To support long and high-quality streams,one viable approach is that a media stream is partitioned into segments,and then the segments are replicated in a network and served in a peer-to-peer(P2P) fashion. However,the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ants-like search algorithm(HASA) for P2P media streaming distribution in ad hoc networks. It takes the advantages of random walks and ants-like algorithms for searching in unstructured P2P networks,such as low transmitting latency,less jitter times,and low unnecessary traffic. We quantify the performance of our scheme in terms of response time,jitter times,and network messages for media streaming distribution. Simulation results showed that it can effectively improve the search efficiency for P2P media streaming distribution in ad hoc networks.

RESEARCH ON GROUPING STRATEGY OF SIP-BASED STREAMING MEDIA P2P LIVE BROADCAST NETWORK

The rapid development of Internet has led to the explosion of information sharing, and how to supervise the sharing is a main research topic on current Internet. Aiming at the disadvantage that the current Peer-to-Peer (P2P) is hard to manage and control, this paper presents a Session Initial Protocol (SIP)-based P2P network of three-level architecture. SIP middleware is introduced to the middle level of the three-layer architecture. By the connection function of the SIP signaling, the P2P transmission on media-level can be controlled. Using SIP’s register and authentication function, the manage layer can manage the whole P2P network. Based on the aforementioned architecture, this paper investigates the grouping strategy on a live broadcast application in P2P network. Combined with the function of SIP register, the paper works on several grouping strategies, sets up models to manage users by grouping them, presents a weight-based K-means IP address grouping algorithm, and realizes it. The experiment shows that the grouping strategy presented in this paper can solve the problem of group sharing of network resource, and can realize the efficient-sharing, reasonable-distributing of network resource.

An ALM Model for Streaming Media Service in P2P-SIP Network

Application layer multicast （ALM） has been widely applied in Internet, as a substitute for IP multicast. However, it causes network load to increase because it adopts unlcast in data transmission, which limits the application. In this article, in order to improve the ALM performance in P2P-SIP network, an ALM model was proposed which reduced network load via merging messages in concast mode. Finally network simulations prove that this model has better benefit on single node load and average network load. Therefore, it is suitable for streaming media service in P2P-SIP Network.

面向实时交互的P2P Streaming模型

实施大规模分布式可视化操纵系统的一个重要瓶颈在于节点间海量数据的传输。P2P Streaming技术逐渐成为解决该瓶颈的可行方案之一。主流P2P Streaming模型由于实时性上的缺陷并不适于此类系统。该文通过引入服务质量的概念并采用启发式的算法,设计一种改良的P2P Streaming模型。仿真实验结果表明,新模型在平均延迟、首包到达时间等指标上优于现有模型。

GPSA:A Greedy Pull-based Scheduling Approach for P2P Live Streaming under Heterogeneous Environments

Pull-based P2P live streaming is a promising solution for the large scale streaming systems, like PPStream, PPlive, due to its high scalability, low cost and high resilience. However, they usually suffer from bad delay performance. In this paper, we seek to improve the delay performance under ensuring video display quality stemming from chunk scheduling. And so we model Pull-based chunk scheduling problem as a multi-objective optimization problem to minimize the video delay and maximize video display quality in the environment of heterogeneous upload bandwidths, heterogeneous and dynamic propagation delays. Finally we put up with a greedy Pull-based scheduling approach(GPSA) to solve the optimization problem. The evaluation shows GPSA can outperform two classical chunk scheduling approaches and adapt to dynamic variance of propagation delays.

P2P Streaming Traffic Classification in High-Speed Networks

The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper.By analyzing traffic statistical features and network behavior of P2P streaming,a group of flow characteristics were found,which can make P2P streaming more recognizable among other applications.Attributes from Netflow and those proposed by us are compared in terms of classification accuracy,and so are the results of different sampling rates.It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system.Even with 1:50 sampling rate,the recognition accuracy can be higher than 94%.Moreover,we have evaluated the CPU resources,storage capacity and time consumption before and after the sampling,it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.

Content subscribing mechanism in P2P streaming based on gamma distribution prediction

P2P systems are categorized into tree-based and mesh-based systems according to their topologies. Mesh-based systems are considered more suitable for large-scale lnternet applications, but require optimization on latency issue. This paper proposes a content subscribing mechanism （CSM） to eliminate unnecessary time delays during data relaying. A node can send content data to its neighbors as soon as it receives the data segment. No additional time is taken during the interactive stages prior to data segment transmission of streaming content. CSM consists of three steps. First, every node records its historical segments latency, and adopts gamma distribution, which possesses powerful expression ability, to express latency statistics. Second, a node predicts subscribing success ratio of every neighbor by comparing the gamma distribution parameters of the node and its neighbors before selecting a neighbor node to subscribe a data segment. The above steps would not increase latency as they are executed before the data segments are ready at the neighbor nodes. Finally, the node, which was subscribed to, sends the subscribed data segment to the subscriber immediately when it has the data segment. Experiments show that CSM significantly reduces the content data transmission latency.

Peer priority based data scheduling algorithm in P2P streaming system

In order to solve the problem that the existing data scheduling algorithm cannot make full use of neighbors' bandwidth resources when allocating data request among several senders in the multisender based P2P streaming system,a peer priority based scheduling algorithm is proposed.The algorithm calculates neighbors' priority based on peers' historical service evaluation as well as how many wanted data that the neighbor has.The data request allocated to each neighbor is adjusted dynamically according to the priority when scheduling.Peers with high priority are preferred to allocate more data request.Experiment shows the algorithm can make full use of neighbors' bandwidth resources to transmit data to reduce server pressure effectively and improve system scalability.

Benefiting from providing multi-dimensional view of peers in overlay of P2P streaming system

Most overlay of existing P2P streaming systems just focus on the view point of video content data.An multi-dimensional overlay for the P2P streaming system(MDOPS) is proposed for providing multi-dimensional view including video data,peers' service capability and online stability based on locality sensitive hashing.MDOPS organizes all Live/VoD peers and the above multi-dimensional information in a one-dimensinal DHT,uses range resource information publish/search and introduces multiple load balancing methods.MDOPS maintains an additional candidate coordinating peer list with high qualified peers who own the video data the peer would possibly access currently and in future.This list could speed up the process of searching peers for data scheduling layer.Simulation experiment based on trace of real streaming system has testified that MDOPS can effectively improve the quality of search results and smooth load distribution among peers without increasing the cost of resource publish/search.

Network address translation traversal based on Bernoulli laws of large number in P2P streaming system

P2P streaming application must realize network address translation （NAT） traversal. To handle low success ratio of the existing NAT traversal algorithm, UPnP-STUN （UPUN） and port-mapping sample estimation （PMSE） algorithm are recommended in this paper. UPUN is the combination of UPnP and STUN, and PMSE utilizes port mapping samples added by symmetric NAT for different sessions to estimate regularity of port mapping of symmetric NAT, which takes advantage of the Bernoulli law of large numbers. Besides, for the situation that both peers are behind NAT, and to handle heavy relay server load when many inner peers want to communicate with each other, a peer auxiliary-relay （PAR） algorithm is presented. PAR lets outer peers with sufficient bandwidth act as relay servers to alleviate pressure of real server, which could avoid NAT traversal failure caused by single point failure of relay server. Finally, experiments show that the proposed algorithms could improve the success ratio significantly for NAT traversal in P2P streaming application as well as improve P2P streaming application applicability.

Borrower’s default and self-disclosure of social media information in P2P lending

Background:We examine the signaling effect of borrowers’social media behavior,especially self-disclosure behavior,on the default probability of money borrowers on a peer-to-peer(P2P)lending site.Method:We use a unique dataset that combines loan data from a large P2P lending site with the borrower’s social media presence data from a popular social media site.Results:Through a natural experiment enabled by an instrument variable,we identify two forms of social media information that act as signals of borrowers’creditworthiness:(1)borrowers’choice to self-disclose their social media account to the P2P lending site,and(2)borrowers’social media behavior,such as their social network scope and social media engagement.Conclusion:This study offers new insights for screening borrowers in P2P lending and a novel usage of social media information.

Multiple description coding with spatial-temporal hybrid interpolation for video streaming in peer-to-peer networks

In this paper, we present an innovative design of multiple description coding with spatial-temporal hybrid interpola- tion (MDC-STHI) for peer-to-peer (P2P) video streaming. MDC can be effective in P2P networks because the nature of overlay routing makes path diversity more feasible. However, most MDC schemes require a redesign of video coding systems and are not cost-effective for wide deployment. We base our work on multiple state video coding, a form of MDC that can utilize standard codecs. Two quarter-sized video bit streams are generated as redundancies and embedded in the original-sized streams. With MDC-STHI, the nodes in P2P network can adjust the streaming traffic to satisfy the constraints of their devices and network environment. By design, the redundancies are used to compensate for missing frames, and can also be streamed independently to fulfill certain needs of low rate, low resolution applications. For better error concealment, optimal weights for spatial and temporal interpolation are determined at the source, quantized, and included in redundancies.

Improving Playback Quality of Peer-to-Peer Live Streaming Systems by Joint Scheduling and Distributed Hash Table Based Compensation

In Peer-to-Peer(P2P) streaming systems,video data may be lost since peers can join and leave the overlay network randomly,thereby deteriorating the video playback quality.In this paper we propose a new hybrid mesh and Distributed Hash Table(DHT) based P2P streaming system,called HQMedia,to provide high playback quality to users by maintaining high data dissemination resilience with a low overhead.In HQMedia,peers are classified into Super Peers(SP) and Common Peers(CP) according to their online time.SPs and CPs form a mesh structure,while SPs alone form a new Streaming DHT(SDHT) structure.In this hybrid architecture,we propose a joint scheduling and compensation mechanism.If any frames cannot be obtained during the scheduling phase,an SDHT-based compensation mechanism is invoked for retrieving the missing frames near the playback point.We evaluate the performance of HQMedia by both theoretical analysis and intensive simulation experiments on large-scale networks to demonstrate the effectiveness and scalability of the proposed system.Numerical results show that HQMedia significantly outperforms existing mesh-based and treebased P2P live streaming systems by improving playback quality with only less than 1% extra maintenance overhead.

Rate-distortion optimized bitstream switching for peer-to-peer live streaming

Peer-to-peer (P2P) technology provides a cost-effective and scalable way to distribute video data. However, high heterogeneity of the P2P network, which rises not only from heterogeneous link capacity between peers but also from dynamic variation of available bandwidth, brings forward great challenge to video streaming. To attack this problem, an adaptive scheme based on rate-distortion optimization (RDO) is proposed in this paper. While low complexity RDO based frame dropping is exploited to shape bitrate into available bandwidth in peers, the streamed bitstream is dynamically switched among multiple available versions in an RDO way by the streaming server. Simulation results show that the proposed scheme based on RDO achieves great gain in overall perceived quality over simple heuristic schemes.

Neighbour Peer Selection Scheme Based on Effective Capacity for Mobile Peer-to-Peer Streaming

For Peer-to-Peer (P2P) streaming services in mobile networks, the selection of appropriate neighbour peers from candidate peers with demanding data is an important approach to improve Quality-of-Service (QoS). This paper proposes a novel Effective Capacity Peer Selection (ECPS) scheme based on effective capacity. In the ECPS scheme, the neighbour peer selection problem was modeled using the Multiple Attribute Decision Making (MADM) theory, which considered multiple factors of candidate peers, including Signal to Interference and Noise Ratio (SINR), residency time, power level, security, moving speed, and effective capacity. This model could increase the suitability of ECPS for wireless mobile environments. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to solve the MADM problem and identify the preferred neighbour peers. Simulation results show that the ECPS scheme can improve the network throughput, reduce packet delay by about 82%, and almost double the packet delivery ratio of the mobile P2P streaming service.

基于P2P的分布式VOD系统的研究

本文构建了基于P2P的分布式VOD系统的应用模型,并以JXTA和Windows Media作为技术支持,对系统中的相应关键问题提出了解决方案及其实现方法,力争用尽量小的资源消耗来提供保障QOS的分布式VOD服务。

RTMFP协议在P2P视频通信中的应用

在深入研究RTMFP协议的工作原理及其实现过程的基础上,分析了该协议的新特性及其在实时通信领域中的优势,并将其应用于P2P视频通信中。结合ActionScript和Flash Media Server等技术和产品,实现了一个P2P视频通信应用原型,并对其具体的应用方法和实现方式进行了探索和阐述。研究表明,该协议提供了一种新的低成本且高质量的P2P视频通信方案,具有一定的实用性和创新性。

基于P2P的媒体数据传输调度策略的研究

在一个流会话中如何将媒体数据分配给多个供应节点,使得接收节点的缓冲延迟最小,这是一个值得研究的问题。本文首先分析研究了两种典型数据分配算法,然后给出了基于M BD S算法的媒体传输调度策略,并通过仿真模拟对比了三种调度策略的性能。

BulkTree: an overlay network architecture for live media streaming

Peer-to-peer (P2P) systems are now very popular. Current P2P systems are broadly of two kinds, structured and unstructured. The tree structured P2P systems used technologies such as distributed hash tables (DHT) and hierarchical clustering can search the required target quickly, however, in a tree, the internal node has a higher load and its leave or crash often causes a large population of its offspring's problems, so that in the highly dynamic Internet environment the tree structure may still suffer frequent breaks. On the other hand, most widely used unstructured P2P networks rely on central directory servers or massive message flooding, clearly not scalable. So, we consider both of the above systems' advantages and disadvantages and realize that in the P2P systems one node may fail easily, but that when a number of nodes organized as a set, which we call "super node", the set is robust. Super nodes can be created and updated aware of topology-aware, and used with simple protocol such as flooding or "servers" to exchange information. Furthermore the entire robust super node can be organized into exquisite tree structure. By using this overlay network architecture, P2P systems are robust, efficient, scalable and secure. The simulation results demonstrated that our architecture greatly reduces the alteration time of the structure while decreasing the average delay time, compared to the common tree structure.

Machine Learning-based Stable P2P IPTV Overlay

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers.Since Internet was not designed for such services during its inception,such a service poses some serious challenges including cost and scalability.Peer-to-Peer(P2P)Internet Protocol Television(IPTV)is an application-level distributed paradigm to offer live video contents.In terms of ease of deployment,it has emerged as a serious alternative to client server,Content Delivery Network(CDN)and IP multicast solutions.Nevertheless,P2P approach has struggled to provide the desired streaming quality due to a number of issues.Stability of peers in a network is one of themajor issues among these.Most of the existing approaches address this issue through older-stable principle.This paper first extensively investigates the older-stable principle to observe its validity in different scenarios.It is observed that the older-stable principle does not hold in several of them.Then,it utilizes machine learning approach to predict the stability of peers.This work evaluates the accuracy of severalmachine learning algorithms over the prediction of stability,where the Gradient Boosting Regressor(GBR)out-performs other algorithms.Finally,this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay.The results indicate that machine learning-based stability estimation significantly improves the system.