P2P Live Streaming Systems Research Articles

WidePLive: a coupled low‐delay overlay construction mechanism and peer‐chunk priority‐based chunk scheduling for P2P live video streaming

In recent years, peer-to-peer (P2P) live streaming is popularised by the scalability and cost-effectiveness of P2P networks. User satisfaction in P2P live streaming systems depends on several factors, including chunk scheduling techniques and overlay construction mechanisms in these systems. P2P live streaming systems are involved with the peer and chunk selection problems to improve quality parameters of streaming, such as playback continuity, startup delay, and playback latency. In this study, WidePLive as a P2P live video streaming system is proposed. In WidePLive, the authors proposed a low-delay overlay construction mechanism and a mixed strategy based chunk scheduling scheme which are coupled together by a contribution-aware peer selection strategy as a coupling feature to improve the quality parameter. The proposed overlay construction mechanism allows new peers to have the opportunity to connect with previous peers near the server and forms a low-depth and low-delay overlay. The proposed chunk scheduling scheme uses the benefits of Rarest First and Greedy strategies to trade-off between quality parameters. The evaluation of WidePLive simulation results demonstrates an acceptable improvement in streaming performance and shows that WidePLive has lower startup delay and playback latency and higher playback continuity compared to previous works.

Content pollution propagation in the overlay network of peer‐to‐peer live streaming systems: modelling and analysis

In the past few years, peer-to-peer (P2P) live streaming systems have gained great commercial success and have become a popular way to deliver multimedia content over the Internet, which received more and more attentions from both industry and academia globally. However, the dramatic rise in popularity makes these systems more likely to be vulnerable targets. In this study, mesh-pull infrastructure architecture and pollution attack principle for P2P live streaming systems were presented firstly, and then the various user behaviours under the pollution attack were analysed. Subsequently, the authors proposed an analytical modelling framework of content pollution attack for P2P live streaming systems. Different from the existing content pollution propagation models, it considers the impact of user behaviours in the attack. Furthermore, to ensure the availability and accuracy of the model, the real-world experimental attack data for a popular commercial system was used to verify it. The results showed that the model is a feasible and efficient tool to analyse and predict content pollution propagation in real-world P2P live streaming systems. The authors' work can provide an in-depth understanding of the content pollution propagation in P2P live streaming systems, and evaluation of restraining illegal content distribution for copyright holders and government.

Analysis of the probability of channel satisfactory state in P2P live streaming systems

In this paper a model based on user behaviour of P2P live streaming systems was developed in order to analyse one of the key QoS parameter of such systems, i.e. the probability of channel-satisfactory state, the impact of upload bandwidths and channels’ popularity on the probability of channel-satisfactory state was also analysed. Results obtained have shown that channels with high number of patronising peers achieve satisfactory state easily while channels with low number of peers hardly attain satisfactory state. This called for the need to design a framework or incentive scheme for effective functionality of P2P live streaming systems which will favour all channels regardless of their popularities. Keywords: P2P Systems, QoS Parameters, Satisfactory State, Video Streaming, Fluid Mode

QuickSwap: A Lightweight Fast Recovery Protocol for Mesh-based P2P Live Streaming

In this paper, we propose a failure recovery protocol named QuickSwap for mesh-based P2P live streaming systems. Unlike the existing mCache-based failure recovery scheme, QuickSwap enables peers to quickly recover their live streaming quality from neighbor failures by exchanging and pre-processing of local streaming information. Our contributions in this work are two-fold. First, QuickSwap’s lightweight streaminginformation exchange protocol together with the proposed neighbor maintenance and streaming-quality estimation methods provide a significant performance edge over the conventional mCache-based failure recovery. Second, we have built a system model for our proposal and have evaluated QuickSwap’s performance under different system settings through simulation methodology. Our results reveal that at a small, affordable overhead cost, QuickSwap protocol can support fast recovery of live streaming quality with a nearly 100% failure recovery success rate and a fail-over time less than one second for an average of exponentially distributed peer lifetimes as low as 10 minutes.

ReDePoly: reducing delays in multi-channel P2P live streaming systems using distributed intelligence

Video streaming over peer-to-peer (P2P) networks is a promising approach for scalable IPTV and online games. Distributing high-quality videos among users who employ numerous channels in multi-channel P2P live streaming systems, however, still suffers from many challenges arisen from the network size and dynamicity, channel resource imbalance, and bandwidth limitations. Other challenges also exist, including instability of peers, the peers’ low participation, large startup and playback delays, low quality of received video, and resource insufficiency in unpopular channels. In this paper, ReDePoly, with some levels of distributed intelligence, is proposed to reduce the bootstrapping delay and to increase the service quality. The key idea is to substitute agents with bootstrapping peers in the channels to model user behaviors and to share the aggregated knowledge among the agents to disseminate the learned models. Accordingly, the agents dynamically predict the behavior of participating peers to pre-assign them to the predicted channels and to parallelize the threads related to viewing behavior and channel switching. In case of congested channels, the new agents are distilled to mitigate the agent responsibilities. Simulation results show that the proposed approach outperforms the other existing methods regarding the channel switching delay, recovery delay, and quality of service.

An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming

Recently, P2P streaming techniques have been a promising solution to a large-scale live streaming system because of their high scalability and low installation cost. In P2P live streaming systems, however, it is difficult to manage peers’ buffers effectively, because they can buffer only a limited amount of data around a live broadcasting time in the main memory and suffer from long playback lag due to the nature of P2P structures. In addition, the number of peers decreases rapidly as the playback position moves further from this time by performing time-shifted viewing. These situations widen the distribution of peers’ playback positions, thereby decreasing the degree of data duplication among peers. Moreover, it is hard to use each peer’s buffer as the caching area because the buffer area where the chunks that have already been played back are stored can be overwritten at any time by new chunks that will arrive soon. In this paper, we therefore propose a novel buffering scheme to significantly increase data duplication in buffering periods among peers in P2P live and time-shifted streaming systems. In our proposed scheme, the buffer ratio of each peer is adaptively adjusted according to its relative playback position in a group by increasing the ratio of the caching area in its buffer as its playback position moves earlier in time and increasing the ratio of the prefetching area as its playback position moves later. Through extensive simulations, we demonstrate that our proposed adaptive buffering scheme outperforms the conventional buffering technique considerably in terms of startup delay, average jitter ratio, and the ratio of necessary chunks in a buffermap.

Access control mechanism using flexible multi‐layer structure for mesh‐based P2P live streaming systems

SummaryWhen thousands of new peers seek to join the peer‐to‐peer (P2P) system within a very short time (i.e., the so‐called flash crowd event), most of the peers suffer a long startup delay as a result of peer over‐competition. Accordingly, recent studies have proposed a slot‐based user access control (UAC) mechanism, which periodically admits a certain number of new peers to the system, and a user batch join (UBJ) mechanism, which preconstructs the new peers into a fixed‐size tree structure before peer join process. However, in the UAC mechanism, it is difficult to determine the optimal time slot length; while in the UBJ mechanism, it is difficult to determine the optimal tree size. Accordingly, the present study proposes a structured access control (SAC) mechanism, in which the new‐arriving peers are preconstructed to a flexible multilayer structure to overcome the limitation of a fixed‐size tree structure, and the number of peers in each layer of the structure is determined based on a consideration of the remaining upload bandwidth of the P2P system. Based on the assumption of a heterogeneous upload bandwidth capability of the peers, an analytical model is derived to describe the growth behavior of a P2P system with access control. It is shown that the simulation results for the growth rate of a P2P system based on the SAC mechanism are in good agreement with those obtained from the analytical model. In addition, the simulation results show that the SAC mechanism outperforms the UAC and UBJ mechanisms in terms of a more rapid system growth and a shorter average startup delay. Copyright © 2016 John Wiley & Sons, Ltd.

VAStream: Virtual adaptive substream partition and scheduling in data-driven P2P live streaming

Substream-based P2P live streaming systems can achieve high performance according to multiple metrics, including distribution delay, system robustness and protocol overhead. However, the current division approach into substreams is not flexible and cannot be adaptive to dynamic network environments, which characterize the basic transport facilities for supporting P2P live streaming services. In this paper, we propose a virtual adaptive substream partition and scheduling protocol, namely VAStream, which can enforce substream partition or merge operation based on the current P2P network status. This protocol also presents properly crafted schemes to deal with the issues in complex network situation, and effectively makes use of peer resources. Comparing with other mechanisms, the simulation results show that VAStream can achieve low distribution delay, high chunk arrival ratio and good system robustness.

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.

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.

Performance Modeling and Evaluation of Peer-to-Peer Live Streaming Systems Under Flash Crowds

A peer-to-peer (P2P) live streaming system faces a big challenge under flash crowds. When a flash crowd occurs, the sudden arrival of numerous peers may starve the upload capacity of the system, hurt its quality of service, and even cause system collapse. This paper provides a comprehensive study on the performance of P2P live streaming systems under flash crowds. By modeling the systems using a fluid model, we study the system capacity, peer startup latency, and system recovery time of systems with and without admission control for flash crowds, respectively. Our study demonstrates that, without admission control, a P2P live streaming system has limited capacity to handle flash crowds. We quantify this capacity by the largest flash crowd (measured in shock level) that the system can handle, and further find this capacity is independent of system initial state while decreasing as departure rate of stable peer increases, in a power-law relationship. We also establish the mathematical relationship of flash crowd size to the worst-case peer startup latency and system recovery time. For a system with admission control, we prove that it can recover stability under flash crowds of any sizes. Moreover, its worst-case peer startup latency and system recovery time increase logarithmically with the flash crowd size. Based on the analytical results, we present detailed flash crowd handling strategies, which can be used to achieve satisfying peer startup performance while keeping system stability in the presence of flash crowds under different circumstances .

HIPPO: An Adaptive, Scalable, Hierarchical P2PLive Streaming System

P2P live streaming is a hot research topic of Internet, but for the defective of system architecture and scheduling policy, P2P streaming applications have poor user experience, such as long start-delay, long play-delay, and low play-continuity and so on. Firstly, based on the analysis of the advantages and disadvantages of previous works, this paper designed and implemented a hierarchical P2P streaming system. The system is composed by three layers: source management servers, region management servers, and peers. The underlying P2P overlay network is organized through Mesh-pull mechanism. We propose a new scheduling strategy to utilize the capacity of the peer. The new adaptive scheduling strategy considers the urgency and scarcity of the data blocks, and selects the appropriate neighbor nodes by monitoring neighbor nodes’ the maximum load. The study of a real-life system provides valuable insights for the future development of P2P live streaming systems.

Threshold bipolar scheduling for P2P live streaming

In P2P live streaming, the startup mechanism used by peers directly determines their streaming performance when they first join a channel or recover from playback freezes. However, there has been no systematic study of peer behaviors and streaming strategies in their startup stages. Motivated by a measurement study of peer startup behaviors in real P2P live streaming systems, we propose a simple and effective hybrid chunk fetching strategy, called Threshold Bipolar (TB), which partitions the buffer of a peer into head and tail parts and employs different chunk download strategies for the two parts. According to the TB strategy, a peer downloads chunks in the head part using selfish strategies, like the Greedy. Once the head part is fully filled, it needs to use altruistic fetching strategies, like the Rarest First or the Random, to download chunks in the tail part. We study the design choices of the major parameters of the proposed TB strategy. In particular, we propose to dynamically adjust the TB threshold to tradeoff between a peer’s selfishness and altruism based on the system-wide streaming performance. We further develop analytical models to characterize the buffer progresses of peers in startup stages. Through extensive simulations, we demonstrate that the proposed TB strategy outperforms the existing P2P chunk fetching strategies and is robust against flash-crowd.

Node Classification Concurrent Transmission Algorithm Based on P2P Streaming Media

P2P Live streaming systems is unstructured mesh topology, except satisfying large-scale concurrent node access, it would also lead to long transmission delay due to excessive number of times of data transmission. In order to reduce the number of times of data transmission in the network, the node classification concurrent transmission algorithm is suggested in this paper, which makes the peer-level nodes concurrent to initiatively transmit data, avoiding the inefficient bandwidth utilization of random transmission request of node and high management cost; meanwhile, each level will continue to transmit data to its next-level nodes, triggering and continuously accelerating the concurrent transmission within this level, thus to achieve the maximum use of bandwidth and reduce the overall delay of transmission. Analyze by conducting mathematical modeling, when the data content is divided into enough copies by classification concurrent transmission algorithm, the total time unit spend in transmitting data blocks to all of the nodes and the average time unit spent on nodes are superior to those of the existing transmission algorithms.

Exploring the optimal substream scheduling and distribution mechanism for data-driven P2P media streaming

Most P2P live streaming systems divide video stream into fine-grained blocks, and paralleled distribute these blocks in overlay network to utilize the bandwidth and storage resources of end users, which improves the scalability. Although transmitting video stream with these blocks greatly guarantees the system robustness, it also brings long distribution delay and high protocol overhead issues. Therefore, aggregating these blocks to substreams and delivering them in data-driven mode can promote the distribution efficiency under the network environment with peer churn. In this paper, we study the substream scheduling and distribution mechanism in data-driven overlay network, and formulate it as a minimax problem. Subsequently, we propose a global centralized approach to solve this problem, and derive the optimal substream scheduling and distribution scheme. According to the design idea of global centralized solution, we present a distributed substream scheduling and distribution mechanism, which is adaptive to dynamic P2P network. Finally, we compare the performance of global centralized solution and distributed substream scheduling and distribution mechanism with block-based live streaming. The experiment results show substream-based design achieves better efficiency in video stream dissemination, and distributed substream scheduling and distribution mechanism can preserve high performance when peer churn appears.

Content-Priority-Aware Chunk Scheduling Over Swarm-Based P2P Live Streaming System: From Theoretical Analysis to Practical Design

In this work, we revisit two chunk, which is the smallest video data unit for scheduling and transmission, scheduling policies that are essentially mutually exclusive but beneficial for swarm-based P2P live streaming systems. The first is content-diversified oriented (cd-oriented) policy, which regards each chunk of equal importance and schedules chunks to be sent in a near-random fashion. With this approach, peers hold different parts of stream content and contribute their available bandwidth to the system. The second is importance-first oriented (if-oriented) policy, which gives each chunk a content-dependent priority, usually in a rate-distortion (RD) sense, and first schedules the highest-priority chunk to be sent. In doing so, important chunks are more likely to be successfully received before their playback; the reconstructed video quality is thus enhanced under poor network conditions. We successfully identify a simple methodology, which operates on the data availability domain, to leverage both policies. (Data availability here means the set of data units that the user can get from its source(s)). This allows us to deploy dynamic strategy switch scheduling in practical systems to further improve the received video quality of each peer. Simulation results show that our data-availability driven dynamic strategy switch not only overcomes the drawbacks of the two individual policies but also retains the benefits of both. Most importantly, it bridges the gap between rate-distortion analysis on compressed video and P2P content delivery research.

P2P 라이브 스트리밍 시스템을 위한 프리패칭/캐싱 통합 기법

Abstract In this paper, we propose a buffering scheme to improve the performance in P2P live streaming systems by adjusting the ratio of caching and prefetching portion of each peer. To this end, we assign all the peers into many groups depending on their playback periods. We then determine the ratio of caching and prefetching portion in each peer depending on its playback time position relative to those of other peers within the same group. In other words, as the playback position of a peer gets later, we increase the ratio of its caching portion. On the contrary, as the playback position of a peer gets eariler, we increase the ratio of its prefetching portion. This can significantly increase the degree of data duplication among peers that belong to each specific group. By simulation experiments, we show that our proposed an integrated prefetching/caching scheme can improve the performance considerably in terms of jitter ratio, initial playback delay and shared buffermap ratio when compared to the existing fixed portion buffering scheme.

Peer startup process and initial offset placement in peer-to-peer (P2P) live streaming systems

We measure and study the peer startup process in P2P live streaming systems and focus on a critical issue in this aspect: How a peer initializes its buffer status when it joins a channel, i.e., initial offset placement of the peer’s buffer in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present two initial offset placement schemes we inferred from the measurement results of real systems: Fixed padding (FP) scheme and proportional placement (PP) scheme. With FP scheme, the initial buffer offset is simply set to the offset reported by the reference neighbor peer of the new peer plus a fixed padding. With PP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer’s offset lag or buffer width. We evaluate the performance of these two schemes, and find that the FP scheme, although simple, is not practical to be adopted in real P2P live streaming systems, and PP scheme is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We then report our detailed measurement results of PPLive and UUSee, two popular commercial P2P streaming systems. The measurement results show that PPLive adopts the PP scheme based on buffer width and has good performance. UUSee, however, adopts FP scheme in its early version and has relatively low performance, and it later upgrades to use the PP scheme in its new version and consequently obtains better performance. These results show that initial offset placement is a critical design issue in P2P live streaming systems, and the PP scheme presented in this paper is a good algorithm for this purpose. Our analytical and measurement results could be useful for guiding the analysis and design of buffering protocols for P2P live streaming systems.

A Decision Theoretic Approach for In-service QoE Estimation and Prediction of P2P Live Video Streaming Systems Based on User Behavior Modeling and Context Awareness

Peer-to-Peer (P2P) systems are fast becoming the most popular live video streaming solutions due to their self-scaling properties, ease of deployment, cost effective nature, content addition and content distribution properties. In order to improve the user related and network related quality of these systems, in-service QoE estimation and prediction is required. In this paper, in-service QoE is estimated and predicted based on decision theoretic approach by modeling the problem as a decision network. The results of this new improved approach for QoE estimation and prediction for P2P live video streaming systems shows that the estimated QoE is not only user-centric and context aware but also dynamic in nature as it changes with the changing situation. The proposed approach calculates the QoE quantitatively and as the results indicate can replaced the existing objective and hybrid QoE estimation approaches.

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.