User density sensitive P2P streaming in wireless mesh networks

Link rate allocation is very important for supporting high video playback rate in Peer-to-Peer video streaming. Although many studies can be found on resource allocation in P2P streaming in wired networks, very few studies have studied the problem in wireless networks, especially in Wireless multi-hop Mesh Networks (WMNs), which is still challenging. To maximize the users’ satisfaction of P2P streaming in WMNs, this paper focuses on link rate allocation problem and proposes a fully distributed algorithm to efficiently utilize the upload and download bandwidth of wireless mesh nodes. We first build an efficient P2P streaming system based on the experimental results from real deployment of our wireless mesh testbed. Then we design an efficient distributed algorithm based on the solution to a linear optimization model, which optimizes towards a user-density-related objective to decide the best streaming rates among peers. Our scheme is resilient to network dynamics that is characteristic in wireless multi-hop peer-to-peer networks. The simulation experiments demonstrate the significant performance enhancement by using the proposed rate allocation algorithm in WMNs.     

An efficient scheduling algorithm for scalable video streaming over P2P networks

During recent years, the Internet has witnessed rapid advancement in peer-to-peer (P2P) media streaming. In these applications, an important issue has been the block scheduling problem, which deals with how each node requests the media data blocks from its neighbors. In most streaming systems, peers are likely to have heterogeneous upload/download bandwidths, leading to the fact that different peers probably perceive different streaming quality. Layered (or scalable) streaming in P2P networks has recently been proposed to address the heterogeneity of the network environment. In this paper, we propose a novel block scheduling scheme that is aimed to address the P2P layered video streaming. We define a soft priority function for each block to be requested by a node in accordance with the block’s significance for video playback. The priority function is unique in that it strikes good balance between different factors, which makes the priority of a block well represent the relative importance of the block over a wide variation of block size between different layers. The block scheduling problem is then transformed to an optimization problem that maximizes the priority sum of the delivered video blocks. We develop both centralized and distributed scheduling algorithms for the problem. Simulation of two popular scalability types has been conducted to evaluate the performance of the algorithms. The simulation results show that the proposed algorithm is effective in terms of bandwidth utilization and video quality.     

A comprehensive study of the use of advertisements as incentives in P2P streaming systems

P2P streaming systems, such as PPLive and PPStream, have become popular services with the widespread deployment of broadband networks. However, P2P streaming systems still face free-riding problems, similar to those that have been observed in P2P file sharing systems. Thus, one important problem in providing streaming services is that of providing appropriate incentives for peers to contribute their upload capacity. To this end, we propose the use of advertisements as an incentive for peers to contribute upload capacity. In the proposed framework, peers enjoy the same quality of streamed media, with the difference in quality of service being achieved through different amounts of advertisements viewed, based on the resource contributions to the system. Moreover, since calculating peers’ contributions accurately is important to successfully deploying such systems, we design a token-based framework to address this problem. An extensive simulation-based study is performed to evaluate the proposed approach. The results demonstrate that our approach provides appropriate incentives for peers to contribute their resources. Furthermore, we explore several characteristics of the token-based mechanism which can provide system developers with insight into efficient development of such systems.     

Robust control in cloud assisted peer to peer live streaming systems

Existing live video streaming systems can be classified as server (cloud) based or as peer-to-peer (P2P). The client–server approach promises stability and (Quality of Service) QoS by incurring expensive bandwidth provision cost on the server. On the other hand, P2P architecture is scalable with low bandwidth and maintenance cost. Here we propose a cloud assisted P2P live streaming architecture which is scalable and stable. In order to achieve this we have developed: (i) a scalable gossip protocol that monitors dynamically the total available bandwidth resources of the participating peers, (ii) a control strategy that dynamically allocates the bandwidth that is required.The first step towards this direction is to create a theoretical model that captures the dynamic relationship between the total bandwidth surplus/deficit and peers’ bandwidth utilization in order to be able to apply a control theoretical approach. Moreover, we quantify the impact of monitoring inaccuracies and peers’ dynamic bandwidth changes and we calculate analytically, as a function of them, the minimum amount of bandwidth overprovision that ensures the undisturbed distribution of the stream. System is evaluated through a detailed simulator of a complete P2P live streaming system and testified the uninterrupted and complete stream delivery even in very adverse bandwidth changes.     

Adaptive topology formation for peer-to-peer video streaming

In this paper we propose an adaptive P2P video streaming framework to address the challenges due to bandwidth heterogeneity and peer churn on the Internet. This adaptive streaming framework consists of two major components, source rate adaptation and adaptive overlay topology formation, to maximize the video quality and fully utilize the overall peer upload capacity. In the source rate adaptation, the video server adapts the video source rate automatically based on the local measurement of peers’ download rates, so that the P2P network is not overloaded beyond its bandwidth capacity and peers are able to achieve smooth video playback. To combat bandwidth heterogeneity, we propose to construct a desirable link-level homogeneous overlay topology using a Markov chain Monte Carlo method, so that peers achieve an equal per-connection upload/download bandwidth. In this link-level homogeneous network, video flows do not encounter any bottlenecks along the delivery paths, and peers achieve high download rates to ensure smooth video playback. We also design a fully distributed algorithm to implement the dual mechanisms of the adaptive topology formation and the source rate maximization. To evaluate the performance of our streaming framework, we conduct both mathematical analysis and extensive simulations. The simulation results confirm our analysis and show that the proposed distributed algorithm is able to maximize the video playback quality with fast convergence.     

Adaptive,incentive and scalable dynamic tree overlay for P2P live video streaming

In this paper, we propose a new multicast tree framework to be used in peer-to-peer (P2P) live video streaming systems. The proposed system, adapts the tree links under high peer churn and runs in a totally distributed manner. In order to provide this dynamism and seamless streaming at the same time, we propose a cross layer design involving scalable video codec, backup parents and hierarchical clusters. The performance of the system is measured in real world environment PlanetLab that has nodes distributed all over the world. The experiments show that the proposed system provides high quality of experience (QoE) in terms of Peak Signal to Noise Ratio (PSNR), playback delay and duration of pauses. The proposed system also provides incentive mechanism to its users.     

COINS: COalitions and INcentiveS for effective Peer-to-Peer downloads

Although the proportion of Peer-to-Peer (P2P) traffic has declined in recent years in favor of video streaming and one-click hosting, P2P traffic is predicted to increase in the years to come. Moreover, P2P principles can be applied to improve both, video streaming and one-click hosting, so, P2P is going to continue to be an active research area. This paper presents COINS, a new coalition-formation and incentive mechanism based on game theory. This mechanism encourages cooperative behavior, since contributing peers receive in return a better quality of service, whilst preventing the free-riding problem. To achieve this, we define a “Responsiveness bonus” that reflects the overall contribution of the peer to the system, and we use the game theory utility concept to calculate it. Empirical results, obtained through simulations, show the ability of the coalitions to effectively stop free-riding and promote contribution, increasing the performance of the P2P network and obtaining an improvement in download time. In addition, we prove that users that pursue self-interested strategies, and that contribute, benefit the system.     

Auction-Based On-Demand P2P Min-Cost Media Streaming with Network Coding

Realizing on-demand media streaming in a Peer-to-Peer (P2P) fashion is more challenging than in the case of live media streaming, since only peers with close-by media play progresses may help each other in obtaining the media content. The situation is further complicated if we wish to pursue low aggregated link cost in the transmission. In this paper, we present a new algorithmic perspective toward on-demand P2P streaming protocol design. While previous approaches employ streaming trees or passive neighbor reconciliation for media content distribution, we instead coordinate the streaming session as an auction where each peer participates locally by bidding for and selling media flows encoded with network coding. We show that this auction approach is promising in achieving low-cost on-demand streaming in a scalable fashion. It is amenable to asynchronous, distributed, and lightweight implementations, and is flexible to provide support for random-seek and pause functionalities. Through extensive simulation studies, we verify the effectiveness and performance of the proposed auction approach, focusing on the optimality in overall streaming cost, the convergence speed, and the communication overhead.     

Scalable playback rate control in P2P live streaming systems

Current commercial live video streaming systems are based either on a typical client–server (cloud) or on a peer-to-peer (P2P) architecture. The former architecture is preferred for stability and QoS, provided that the system is not stretched beyond its bandwidth capacity, while the latter is scalable with small bandwidth and management cost. In this paper, we propose a P2P live streaming architecture in which by adapting dynamically the playback rate we guarantee that peers receive the stream even in cases where the total upload bandwidth changes very abruptly. In order to achieve this we develop a scalable mechanism that by probing only a small subset of peers monitors dynamically the total available bandwidth resources and a playback rate control mechanism that dynamically adapts playback rate to the aforementioned resources. We model analytically the relationship between the playback rate and the available bandwidth resources by using difference equations and in this way we are able to apply a control theoretical approach. We also quantify monitoring inaccuracies and dynamic bandwidth changes and we calculate dynamically, as a function of these, the maximum playback rate for which the proposed system able to guarantee the uninterrupted and complete distribution of the stream. Finally, we evaluate the control strategy and the theoretical model in a packet level simulator of a complete P2P live streaming system that we designed in OPNET Modeler. Our evaluation results show the uninterrupted and complete stream delivery (every peer receives more than 99 % of video blocks in every scenario) even in very adverse bandwidth changes.     

Cooperative Media Data Streaming with Scalable Video Coding

Live peer-to-peer (P2P) streaming has become a promising approach for broadcasting non-interactive media content from a server to a large number of interested clients. However, it still faces many challenges such as high churn rate of peer clients, uplink bandwidth constraints of participating peers, and heterogeneity of client throuput capacities. This paper presents a new P2P network called LSONet, a collaborative peer-to-peer streaming framework for scalable layer-encoded bit streams. The contributions are the combination of the advantages of both layered conding and mesh-based packet exchange. With layered coding, it overcomes overlay bandwidth limitatioins and heterogeneity of client capacities. With mesh based overlay streaming, it can better handle peer churns, as compared to tree-based solutions. For achieving these targets, this paper employs a gossip-based data-driven scheme for partnership formation, and proposes two algorithms, optimized transmission policy (OTP) and graceful degradation scheme (GDS), for multi-layers allocation. The proposed system is completely self-organizing, and in a fully distributed fashion. Extensive simulations show that LSONet achieves higher quality of service by peer-assisted streaming and layered video coding. Also, through comparison, results show that the system outperforms some previous schemes in resource utilization and is more robust and resilient for nodes departure, which demonstrate that it is well-suited for quality adaptive live streaming applications.     

一种基于P2P的短视频分享系统

现有在线短视频分享策略通常采用C/S架构,给视频服务器带来较大的带宽压力。为此,提出一种采用点对点方式的在线短视频分享系统IShare,该系统结合用户点播偏好和视频文件之间的社会网络特性实现视频分享。IShare主要包括基于点播兴趣的节点分簇和视频数据源节点的查找2个核心技术。实验结果表明,IShare具备较好的视频数据源节点查找能力,可降低视频服务器带宽资源消耗。     

Incorporating contribution-awareness into mesh-based Peer-to-Peer streaming systems

While Peer-to-Peer streaming has become increasingly popular over the Internet during recent years, the proper allocation of available resources among peers in a resource constraint environment, remains a challenging problem. In a resource constraint environment, the allocated resources and thus delivered quality to individual peers should be proportional to their contribution to the system, i.e., resource allocation should be contribution aware. This in turn results in fairness among peers and encourages active contribution from participating peers which is essential for scalability of P2P systems. However, contribution-aware resource allocation is challenging due to the distributed and dynamic nature of resources in P2P systems. In this paper, we present a tax-based contribution-aware scheme for live mesh-based P2P streaming approaches. In our proposed scheme, individual peers use a tax function to determine their number of parent peers (i.e., their share of resources) based on the number of their child peers (i.e., peers’ contributed resources) and the aggregate available resources in the system. We examine the behavior of a commonly used tax function, and describe how the contribution aware scheme can leverage the tax function. Through extensive simulations we demonstrate the ability of our proposed scheme to properly allocate available resources among participating peers over a wide range of scenarios. We show that the amount of resources (i.e., bandwidth) is divided across peers proportional to their contribution and in our default simulation setting the median delivered quality to high bandwidth peers with high contribution is improved by 100%. We believe that our results shed an insightful light on the dynamics of resource utilization and allocation in the context of live mesh-based P2P streaming.     

Adaptive content-and-deadline aware chunk scheduling in mesh-based P2P video streaming

In mesh-based Peer-to-Peer (P2P) live video streaming systems packet scheduling is an important factor in overall video playback quality. In mesh based P2P video streaming systems, each video sequence is divided into chunks, which are then distributed by multiple suppliers to the receivers. The suppliers need to be coordinated by the receiver through specifying a transmission schedule for each of them. Many previous studies on scheduling of P2P streaming tend to mainly focus on networking issues which strongly depend on a particular P2P architecture such as tree or mesh. These algorithms suffer from some design issues: 1) they are too complex to deploy, 2) they do not take video characteristics into account and 3) they do not have sender-side transmission policy. To address all three of these problems, we propose a new chunk scheduling scheme which consists of two parts: i) receiver-side scheduler and ii) sender-side transmission order scheme. The proposed receiver-side scheduler considers the contribution level of each video frame as well as the frame’s urgency in order to define a priority for each video frame. It attempts to request frames with highest priority from peers which can deliver them in a shorter time. We also design a new chunk transmission order scheme that decides which requested chunk will be sent out first based on its importance to the requesting neighbor. Our simulation results show that the proposed scheduling scheme improves the overall quality of the perceived video in mesh-based P2P video streaming architectures substantially.     

一种分层P2 P流媒体系统重叠网的构建策略

基于Peer-to-Peer(P2P)技术的流媒体应用具有部署效率高及可扩展性好等突出优势。而采用分层视频编码技术的P2P流媒体系统把原视频流分解为多个视频层数据进行分发,让节点能够选择与自己带宽资源相匹配的视频质量,使其很好地适应节点的异构性。但是各分层视频数据传输的路径存在较大差异,使重叠网构建策略面临更大的挑战。因此定义了基于分层视频编码技术环境下的P2P流媒体重叠网络构建问题,并证明该问题是一个NP难问题。提出了一种构建重叠网的集中启发式算法,同时还提出了一种基于视频组(Streaming Group)的分布式重叠网络构建策略。通过大规模网络仿真实验验证了基于该分布式重叠网构建策略的分层流媒体系统具备低服务器带宽占用、高数据获取率等优点。     

Distributed Throughput Maximization in P2P VoD Applications

In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In this paper, we present a systematic study on the throughput maximization problem in P2P VoD applications. We apply network coding to scalable P2P systems to eliminate the delivery redundancy. Since each peer receives distinct packets, a peer with a higher throughput can reconstruct the video at a higher quality. We maximize the throughput in the existing buffer-forwarding P2P VoD systems using a fully distributed algorithm. We demonstrate in the simulations that the proposed distributed algorithm achieves a higher throughput compared to the proportional allocation scheme or the equal allocation scheme. The existing buffer-forwarding architecture has a limitation in total upload capacity. Therefore we propose a hybrid-forwarding P2P VoD architecture to improve the throughput by combining the buffer-forwarding approach with the storage-forwarding approach. The throughput maximization problem in the hybrid-forwarding architecture is also solved using a fully distributed algorithm. We demonstrate that the proposed hybrid-forwarding architecture greatly improves the throughput compared to the existing buffer-forwarding architecture. In addition, by adjusting the priority weight at each peer, we can implement the differentiated throughput among different users within a video session in the buffer-forwarding architecture, and the differentiated throughput among different video sessions in the hybrid-forwarding architecture.      

A Payment-Based Incentive and Service Differentiation Scheme for Peer-to-Peer Streaming Broadcast

We propose a novel payment-based incentive scheme for peer-to-peer (P2P) live media streaming. Using this approach, peers earn points by forwarding data to others. The data streaming is divided into fixed length periods; during each of these periods peers compete with each other for good parents (data suppliers) for the next period in a first-price-auction-like procedure using their points. We design a distributed algorithm to regulate peer competitions, and consider various individual strategies for parent selection from a game theoretic perspective. We then discuss possible strategies that can be used to maximize a peer's expected media quality by planning different bids for its substreams. Finally, in order to encourage off-session users to remain staying online and continue contributing to the network, we develop an optimal data forwarding strategy that allows peers to accumulate points that can be used in future services. Simulations results show that proposed methods effectively differentiate the media qualities received by peers making different contributions (which originate from, for example, different forwarding bandwidths or servicing times), and at the same time maintaining a high overall system performance.     

Content-adaptive wireless streaming of instructional videos

Video streaming over wireless networks is becoming increasingly important for a variety of applications. To accommodate the dynamic change of wireless network bandwidths, Quality of Service (QoS) scalable video streams need to be provided. This paper presents a system of content-adaptive streaming of instructional (lecture) videos over wireless networks for E-learning applications. We first provide a real-time content analysis method to detect and extract content regions from instructional videos, then apply a “leaking-video-buffer” model to adjust QoS of video streams dynamically based on video content. In content-adaptive video streaming, an adaptive feedback control scheme is also developed to transmit properly compressed video streams to video clients not only based on network bandwidth, but also based on video content and the preferences of users. Finally, we demonstrate the scalability and content adaptiveness of the proposed video streaming system with experimental results on several instructional videos.     

Content-adaptive wireless streaming of instructional videos

Video streaming over wireless networks is becoming increasingly important for a variety of applications. To accommodate the dynamic change of wireless network bandwidths, Quality of Service (QoS) scalable video streams need to be provided. This paper presents a system of content-adaptive streaming of instructional (lecture) videos over wireless networks for E-learning applications. We first provide a real-time content analysis method to detect and extract content regions from instructional videos, then apply a “leaking-video-buffer” model to adjust QoS of video streams dynamically based on video content. In content-adaptive video streaming, an adaptive feedback control scheme is also developed to transmit properly compressed video streams to video clients not only based on network bandwidth, but also based on video content and the preferences of users. Finally, we demonstrate the scalability and content adaptiveness of the proposed video streaming system with experimental results on several instructional videos.     

A P2P streaming architecture supporting scalable media

Peer-to-peer (P2P) streaming solutions are changing the way real-time multimedia transmission is distributed over the Internet. The advances in video coding, like Scalable Video Coding, also turns possible high-quality/definition video distribution and consumption. The recent push in using the standard HTTP protocol for streaming videos in the World Wide Web, is also making converged entertainment services come closer to global deployment across TV, Tablet and mobile devices. The combination of Scalable Video, P2P networks and Web technologies for transmitting high-quality live and time-shifted media contents, is a potential area of innovation and a very actual research topic. This paper describes the architecture of a quality-centric P2P distribution network supporting adaptive and scalable streamable media, that decouples the transport mechanisms from the media content type and structure (video, audio, timed-data, timed-text), irrespective of their encoding schemes. The set of P2P streaming protocols designed for the network enables streaming of live and on-demand media, with very low signaling cost. Prototypes of the P2P network components were implemented and integrated in the streaming platform of EU FP7 SARACEN Project. A suite of tests for evaluation of the performance of the solution demonstrates that it keeps a fairly stable quality level with reduced amplitude and frequency of variations, raising the overall quality perceived by the end-user.