Upload Bandwidth Research Articles

Enabling P2P One-View Multiparty Video Conferencing

Multiparty video conferencing (MPVC) facilitates real-time group interaction between users. While P2P is a natural delivery solution for MPVC, a peer often does not have enough bandwidth to deliver her video to all other peers in the conference. Recently, we have witnessed the popularity of one-view MPVC, where each user only watches full video of another user. One-view MPVC opens up the design space for P2P delivery. In this paper, we explore the feasibility of a pure P2P solution for one-view MPVC. We characterize the video source rate region achievable through video relays between peers. For both homogeneous and heterogeneous MPVC systems, we establish tight universal video rate lower bounds that are independent of the number of peers, the number of video sources, and the specific viewing relations between peers. We further propose, P2P video relay designs to approach the maximal video rate region. Through numerical simulations, we verified that the derived lower bounds are indeed tight bounds, and the proposed bandwidth allocation algorithm can achieve a close-to-optimal peer upload bandwidth utilization. Our results demonstrate that P2P is a promising solution for one-view MPVC. Insights obtained from our study can be used to guide the design of P2P MPVC systems.

Two Dimensional Array Based Overlay Network for Balancing Load of Peer-to-Peer Live Video Streaming

The live video data is streaming usually in a tree-based overlay network or in a mesh-based overlay network. In case of departure of a peer with additional upload bandwidth, the overlay network becomes very vulnerable to churn. In this paper, a two dimensional array-based overlay network is proposed for streaming the live video stream data. As there is always a peer or a live video streaming server to upload the live video stream data, so the overlay network is very stable and very robust to churn. Peers are placed according to their upload and download bandwidth, which enhances the balance of load and performance. The overlay network utilizes the additional upload bandwidth of peers to minimize chunk delivery delay and to maximize balance of load. The procedure, which is used for distributing the additional upload bandwidth of the peers, distributes the additional upload bandwidth to the heterogeneous strength peers in a fair treat distribution approach and to the homogeneous strength peers in a uniform distribution approach. The proposed overlay network has been simulated by Qualnet from Scalable Network Technologies and results are presented in this paper.

Asymptotic Properties of Sequential Streaming Leveraging Users' Cooperation

We consider a communication system in which a given digital content has to be delivered sequentially at constant rate to a set of users who asynchronously request it according to a Poisson process. Users can retrieve data: 1) from one or more sources that statically store the entire content; and 2) from users who have previously requested the content, and contribute (for limited time) a random amount of upload bandwidth to the system. We propose a stochastic fluid framework that allows characterizing the aggregate streaming rate necessary at the sources to satisfy all active requests. In particular, we establish the conditions under which the system becomes asymptotically scalable as the number of users grows. Our theoretical results apply to increasingly popular video-on-demand systems exploiting users' cooperation.

Performance Bounds for P2P Streaming System with Transcoding

In this paper, we develop a fluid model that seeks to expose the fundame ntal characteristics and mathematical theory of peer- to-peer streaming system with transcoding. We find out and prove that, to p rovide peers receiving data above some flow rate, there is a lower bound of server upload bandwidth in this kind of system. We give a fl ow rate allocation algorithm to achieve the minimal server upload bandwidth in the proof. We compare this lower bound with the minimal demand of server upload bandwidth of no transcoding system. And, we prove that, the demand of server upload bandwidth in transcoding peer-to-peer streaming system using the proposed algorithm is the necessary (not sufficient) condition for the no transcodin g one. At last, we give the simulations experiment to show the difference of server load in transcoding and no transcoding systems.

Liquidstream II—Scalable P2P overlay optimization with adaptive minimal server assistance for stable and efficient video on demand

In recent years, a number of research efforts have focused on effective and stable P2P architectures aiming at large scale and low bandwidth cost, real time video streaming systems. Particularly, live streaming and Video-on-Demand (VoD) systems have lately attracted much interest. While many efforts mainly focused on the development of distributed schedulers for content exchange among the participating peers, there are still open fundamental questions regarding the graph (P2P overlay) structure that determines the set of peers with which each peer communicates. Additionally, the way that cloud media servers support these systems is still an open issue. In this paper, we consider a BitTorrent-like VoD system and focus on the following questions: (1) how a distributed-scalable architecture can be formed in a highly heterogeneous and dynamic environment such that it dynamically manages the P2P overlay by means of exploiting the available upload bandwidth of the participating peers, and (2) how a cloud media (bandwidth provider) server reduces the amount of the bandwidth percentage contributed to the system in order to support a larger number of peers. To illustrate the success of our approach, we have developed an extensive P2P VoD simulator for demonstrating the efficiency, scalability and stability of our work under variant and dynamic conditions. Our system guarantees the uninterrupted and efficient video distribution, even in extreme cases of peer arrivals and departures at high rates.

Locality-aware streaming in hybrid P2P-cloud CDN systems

The cloud CDN has been exploited as a cost-effective and elastic way for video streaming, where the content providers only pay for resources (e.g., bandwidth, storage, Web service) that they use. As a server-client solution, the cost on the content providers, especially the bandwidth cost for delivering videos from the cloud to the users, is still high. To further mitigate the content distribution cost, it is promising to deploy the P2P streaming technology in conjunction with a cloud CDN, to construct a hybrid P2P-cloud CDN system. In this paper, we focus on optimal, locality-aware video-on-demand streaming solutions in a hybrid P2P-cloud CDN system, which achieve the best tradeoff between the costly bandwidth consumption on the cloud servers and the undesirable inter-ISP traffic incurred in the entire system. Especially, we characterize the demanded upload bandwidth in the cloud CDN and the incurred inter-ISP traffic through a number of stochastic and optimization models. We first apply a loss network model to derive the needed bandwidth capacity in the cloud CDN under any given chunk distribution pattern among the peer caches and any streaming request dispatching strategy among the ISPs, and derive the optimal peer caching and request dispatching strategies which minimize the bandwidth demand on the cloud CDN. We then investigate the necessary volume of inter-ISP traffic required to achieve the minimum cloud bandwidth, as well as the impact of limiting the inter-ISP traffic on the cloud bandwidth consumption. Based on the fundamental insights from our models and analytical results, we design a locality-aware, hybrid P2P-cloud streaming protocol, and validate its performance using extensive simulations under realistic settings.

A P4P-integrated data-driven P2P system for the live multimedia streaming service

The Proactive network Provider Participation for the P2P (P4P) architecture deploys central servers, which perceives network status and provides peering suggestions to P2P systems in order to achieve better network resource utilization while supporting best possible application performance. However, P4P alone may not be able to make appropriate peering suggestions for live multimedia streaming since it does not include mechanisms to reflect some of the parameters that are important to the QoS of live multimedia streaming such as upload bandwidth and stability of a peer as a stream deliverer. Furthermore, peer synchronization and parent replacement in the middle of a session, which are critical issues to the QoS of live multimedia streaming, are also left as the matters to be dealt with by the P2P systems alone. Most of the existing data-driven P2P systems leverage periodic information exchanges among neighboring peers in order to cope with these problems, which may incur long delay and high control overhead. In this paper, we proposed P4P-integrated data-driven P2P system for live multimedia streaming service. The proposed system includes not only the peering suggestion mechanism appropriate for live multimedia streaming but also the peer synchronization and parent replacement mechanisms, which exploit the centralized P4P framework and do not require periodic control information exchanges. We implemented the system in NS-2 simulator and compared its performance to the P4P and existing data-driven P2P systems. The results from experiments show that the proposed system enhances QoS compared to the existing data-driven P2P systems while maintaining the same level of network efficiency of the original P4P.

Effective Utilization of User Resources in PA-VoD Systems with Channel Heterogeneity

Nowadays, peer-assisted video on-demand (PA-VoD) systems offer high-definition (HD) channels to online users. However, the quality of service in such HD channels is usually not comparable to the standard-definition (SD) ones, as HD channels have to seek more bandwidth support and cache space from peers, which is a challenging task. In this paper, we focus on peer cache and upload bandwidth management at the same time for multi-channel PA-VoD systems with heterogeneous video playback rates, i.e., HD and SD channels coexist with different bandwidth and cache requirements. We first take user viewing behaviors into account and derive the statistical performance bounds on server bandwidth consumption, which lead to the conclusion that such behaviors can easily affect the provisioning for HD channels, even if there is enough upload bandwidth from SD peers. We then formulate bandwidth allocation as a linear programming problem to calculate the tight lower bound at any time instant, with global information available and system-wide coordination possible (e.g., through a tracker). Next, we design heuristic algorithms for peer cache replacement and upload bandwidth allocation to fit with the nature of a P2P structure, and the results are compared with the statistical and instance performance bounds through extensive simulation, which shows the efficacy of the proposed algorithms in dynamic scenarios.

A Scheduler Designed for the P2P Live Streaming System

P2P(peer to peer) live streaming is currently a popular research topic, but for the defective of system architecture and scheduling policy, existing P2P streaming applications have poor user experience, such as long startup delay, long playback delay, and low playback continuity. In this paper, we aim at reducing the playback delay from the source in the environment of heterogeneous upload bandwidth, heterogeneous and dynamic propagation delays. We propose a neighbor selection method in order to utilize the capacity of the peer and consider their scheduler playback deadline. This new peering strategy typically leads to low scheduling delays and improve the playback continuity. Finally we apply a receiver driven chunk selection with a mix scheduling algorithm. Through simulation, we can observe that our scheduler can outperform.

Joint bandwidth allocation, data scheduling and incentives for scalable video streaming over peer-to-peer networks

The overall performance of a peer-to-peer (P2P) scalable streaming system largely depends on the strategies employed in bandwidth allocation, data scheduling and incentives. In this paper, we develop a credit-based content-aware bandwidth auction model for scalable streaming in P2P networks. It formulates multi-overlay multi-layer bandwidth request and allocation problems as auction games. Each peer in the games acts as both auctioneer and player. Being a auctioneer, it maximizes the total revenue (credits) by selling upload bandwidth; Being a player, it uses the credits earned in bandwidth sales to sequentially bid for layer bandwidth so as to maximize the received video quality. Also, a content-aware bidding strategy is proposed, under which the required bandwidth quantity from a peer is determined by the informative video chunks and the marginal net utility that peer could provide, as well as the available credits and the maximum layer bit rate. The convergence of the proposed auction algorithm is mathematically proved. Finally, the performance of the proposed scheme is verified by simulation results.

Live Scalable Video Streaming on Peer-to-Peer Overlays with Network Coding

Scalable video coding is a paradigm that allows partial decoding of the video stream at reduced resolution, frame-rate or quality, adapting to display requirements and reception conditions of heterogeneous receivers. Transmission of scalable data with prioritization enhances the transmission performance, reducing the sensitivity to network congestions and exploiting the multirate characteristic of scalable coding. Network coding is a novel transmission technique that allows intermediate network nodes to perform coding operations on the information in transit, as opposed to traditional routing. This yields to maximization of the transmission rate and encoding of the information with spatial diversity. We employ an overlay network that uses network coding and delivers scalable video with prioritization. We test the performance of the scalable streaming against a non-scalable system, when the upload bandwidth of the nodes is not known.

Hybrid fluid modeling approach for performance analysis of P2P live video streaming systems

In this paper a hybrid modeling approach with different modeling formalisms and solution methods is employed in order to analyze the performance of peer to peer live video streaming systems. We conjointly use queuing networks and Fluid Stochastic Petri Nets, developing several performance models to analyze the behavior of rather complex systems. The models account for: network topology, peer churn, scalability, peer average group size, peer upload bandwidth heterogeneity and video buffering, while introducing several features unconsidered in previous performance models, such as: admission control for lower contributing peers, control traffic overhead and internet traffic packet loss. Our analytical and simulation results disclose the optimum number of peers in a neighborhood, the minimum required server upload bandwidth, the optimal buffer size and the influence of control traffic overhead. The analysis reveals the existence of a performance switch-point (i.e. threshold) up to which system scaling is beneficial, whereas performance steeply decreases thereafter. Several degrees of degraded service are introduced to explore performance with arbitrary percentage of lost video frames and provide support for protocols that use scalable video coding techniques. We also find that implementation of admission control does not improve performance and may discourage new peers if waiting times for joining the system increase.

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.

On Replication Algorithm in P2P VoD

Traditional video-on-demand (VoD) systems rely purely on servers to stream video content to clients, which does not scale. In recent years, peer-to-peer assisted VoD (P2P VoD) has proven to be practical and effective. In P2P VoD, each peer contributes some storage to store videos (or segments of videos) to help the video server. Assuming peers have sufficient bandwidth for the given video playback rate, a fundamental question is what is the relationship between the storage capacity (at each peer), the number of videos, the number of peers, and the resultant off-loading of video server bandwidth. In this paper, we use a simple statistical model to derive this relationship. We propose and analyze a generic replication algorithm Random with Load Balancing (RLB) that balances the service to all movies for both deterministic and random (but stationary) demand models and both homogeneous and heterogeneous peers (in upload bandwidth). We use simulation to validate our results for sensitivity analysis and for comparisons to other popular replication algorithms. This study leads to several fundamental insights for P2P VoD system design in practice.

A Distributed Flow Rate Control Algorithm for Networked Agent System with Multiple Coding Rates to Optimize Multimedia Data Transmission

With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.

Improving Streaming Capacity in P2P Live Streaming Systems via Resource Sharing

Peer-to-Peer (P2P) streaming systems provide a large number of channels to users. The streaming capacity for a channel is defined as the maximum streaming rate that can be received by every user in the channel. In the this paper, we study the streaming capacity problem in both tree-based and mesh-based P2P live streaming systems. In tree-based multi-channel P2P live streaming systems, we propose a cross-channel resource sharing approach to improve the streaming capacity. We employ cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we formulate the streaming capacity problem into a resource optimization problem. By solving the resource optimization problem, we can optimally allocate the link rates for each peer to improve the streaming capacity. Through simulations, we demonstrate that the proposed resource sharing approaches can significantly improve the streaming capacity compared to the scheme without resource sharing.

Exploring the Design Space of Multichannel Peer-to-Peer Live Video Streaming Systems

Most of the commercial peer-to-peer (P2P) video streaming deployments support hundreds of channels and are referred to as multichannel systems. Recent research studies have proposed specific protocols to improve the streaming quality for all channels by enabling cross-channel cooperation among multiple channels. In this paper, we focus on the following fundamental problems in designing cooperating multichannel systems: 1) what are the general characteristics of existing and potential designs? and 2) under what circumstances should a particular design be used to achieve the desired streaming quality with the lowest implementation complexity? To answer the first question, we propose simple models based on linear programming and network-flow graphs for three general designs, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-aware bandwidth allocation Approach (PCA), and Active Channel-aware bandwidth allocation Approach (ACA), which provide insight into understanding the key characteristics of cross-channel resource sharing. For the second question, we first develop closed-form results for two-channel systems. Then, we use extensive numerical simulations to compare the three designs for various peer population distributions, upload bandwidth distributions, and channel structures. Our analytical and simulation results show that: 1) the NBA design can rarely achieve the desired streaming quality in general cases; 2) the PCA design can achieve the same performance as the ACA design in general cases; and 3) the ACA design should be used for special applications.

FairTorrent: A Deficit-Based Distributed Algorithm to Ensure Fairness in Peer-to-Peer Systems

Peer-to-peer file-sharing applications suffer from a fundamental problem of unfairness. Free-riders cause slower download times for others by contributing little or no upload bandwidth while consuming much download bandwidth. Previous attempts to address this fair bandwidth allocation problem suffer from slow peer discovery, inaccurate predictions of neighboring peers' bandwidth allocations, underutilization of bandwidth, and complex parameter tuning. We present FairTorrent, a new deficit-based distributed algorithm that accurately rewards peers in accordance with their contribution. A FairTorrent peer simply uploads the next data block to a peer to whom it owes the most data as measured by a deficit counter. FairTorrent is resilient to exploitation by free-riders and strategic peers, is simple to implement, requires no bandwidth overallocation, no prediction of peers' rates, no centralized control, and no parameter tuning. We implemented FairTorrent in a BitTorrent client without modifications to the BitTorrent protocol and evaluated its performance against other widely used BitTorrent clients. Our results show that FairTorrent provides up to two orders of magnitude better fairness, up to five times better download times for contributing peers, and 60%-100% better performance on average in live BitTorrent swarms.

Event-Driven High-Priority First Data Scheduling Scheme for P2P VoD Streaming

The peer churn rate in the peer-to-peer (P2P) video-on-demand streaming service is much higher than in the P2P live streaming service, which makes the data scheduling problem more challenging. First, the available upload bandwidth information used in the data scheduling scheme is often inaccurate due to the peer churn, which lets a peer make bad scheduling decisions and leads to load imbalance. The higher peer churn makes this problem worse. Secondly, the higher peer churn exacerbates the bandwidth contention problem which occurs between a newly joined peer and some already-existing peers. To tackle the above two challenges, we propose an event-driven high-priority first data scheduling scheme, called EHPF scheme. To tackle the first challenge, we design a piggyback mechanism based on the event-driven mechanism. To tackle the second challenge, we design a priority calculation strategy to differentiate the requests from the newly joined peers and those from the already-existing peers, and use the high-priority first policy to allocate the upload bandwidths of peers. Through simulations and a real environment experiment, we demonstrate that the EHPF scheme outperforms the periodical data scheduling scheme in terms of startup delay, streaming quality and load balancing.

Efficient cooperative backup with decentralized trust management

Existing backup systems are unsatisfactory: commercial backup services are reliable but expensive while peer-to-peer systems are cheap but offer limited assurance of data reliability. This article introduces Friendstore, a system that provides inexpensive and reliable backup by giving users the choice to store backup data only on nodes they trust (typically those owned by friends and colleagues). Because it is built on trusted nodes, Friendstore is not burdened by the complexity required to cope with potentially malicious participants. Friendstore only needs to detect and repair accidental data loss and to ensure balanced storage exchange. The disadvantage of using only trusted nodes is that Friendstore cannot achieve perfect storage utilization. Friendstore is designed for a heterogeneous environment where nodes have very different access link speeds and available disk spaces. To ensure long-term data reliability, a node with limited upload bandwidth refrains from storing more data than its calculated maintainable capacity. A high bandwidth node might be limited by its available disk space. We introduce a simple coding scheme, called XOR(1,2), which doubles a node's ability to store backup information in the same amount of disk space at the cost of doubling the amount of data transferred during restore. Analysis and simulations using long-term node activity traces show that a node can reliably back up tens of gigabytes of data even with low upload bandwidth.