Utility Optimal Scheduling and Admission Control for Adaptive Video Streaming in Small Cell Networks

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive video streaming over small cell networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions for a small-cell network.Comment: 5 pages, 4 figures. Accepted and will be presented at IEEE International Symposium on Information Theory (ISIT) 201

Chemical Reaction Optimization for population transition in peer-to-peer live streaming

Peer-to-peer (P2P) live streaming applications are very popular in recent years and a Markov open queueing network model was developed to study the population dynamics in P2P live streaming. Based on the model, we deduce an optimization problem, called population transition problem, with the objective of maximizing the probability of universal streaming by manipulating population transition probability matrix. We employ a chemical reaction-inspired metaheuristic, Chemical Reaction Optimization (CRO), to solve the problem. Simulation results show that CRO outperforms many commonly used strategies for controlling population transition in many practical P2P live streaming systems. This work also shows that CRO also demonstrates the usability of CRO to solve optimization problems. © 2010 IEEE.published_or_final_versionThe IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, 18-23 July 2010. In Proceedings of the IEEE CEC, 2010, p. 1-

A Stable Fountain Code Mechanism for Peer-to-Peer Content Distribution

Most peer-to-peer content distribution systems require the peers to privilege the welfare of the overall system over greedily maximizing their own utility. When downloading a file broken up into multiple pieces, peers are often asked to pass on some possible download opportunities of common pieces in order to favor rare pieces. This is to avoid the missing piece syndrome, which throttles the download rate of the peer-to-peer system to that of downloading the file straight from the server. In other situations, peers are asked to stay in the system even though they have collected all the file's pieces and have an incentive to leave right away. We propose a mechanism which allows peers to act greedily and yet stabilizes the peer-to-peer content sharing system. Our mechanism combines a fountain code at the server to generate innovative new pieces, and a prioritization for the server to deliver pieces only to new peers. While by itself, neither the fountain code nor the prioritization of new peers alone stabilizes the system, we demonstrate that their combination does, through both analytical and numerical evaluation.Comment: accepted to IEEE INFOCOM 2014, 9 page

Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations

In traditional massive content distribution with multiple sessions, the sessions form separate overlay networks and operate independently, where some sessions may suffer from insufficient resources even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities and present a subgradient algorithm which converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no less than the reciprocal of the approximation ratio to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions. The proposed approach and algorithm are expected to be useful for infrastructure-based content distribution networks with long-lasting sessions and relatively stable network environment

An economic analysis of online streaming. How the music industry can generate revenues from cloud computing

This paper investigates the upcoming business model of online streaming services allowing music consumers either to subscribe to a service which provides free-of-charge access to streaming music and which is funded by advertising, or to pay a monthly flat fee in order to get ad-free access to the content of the service accompanied with additional benefits. By imposing a two-sided market model on the one hand combined with a direct transaction between the streaming service and its flat-rate subscribers on the other hand, the investigation shows that it can be highly profitable to launch a business which is free-of-charge for subscribers if advertising imposes a weak nuisance to music consumers. If this is the case, and by imposing an endogenously determined level of advertising which is provided by homogeneous advertisers, we find that a monopolistic streaming service increases the price for its flat-rate subscribers in order to stimulate free-of-charge demand and to capture higher revenues from advertisers. An extension of the model by illegal file-sharing shows that an increase in copyright enforcement shifts rents from music consumers to the monopolist. --Advertising media,Music industry,Online streaming,Piracy

On reducing mesh delay for peer-to-peer live streaming

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to large group. In this paper, we address the design of overlay which achieves low source-to-peer delay, is robust to user churn, accommodates of asymmetric and diverse uplink bandwidth, and continuously improves based on existing user pool. A natural choice is the use of mesh, where each peer is served by multiple parents. Since the peer delay in a mesh depends on its longest path through its parents, we study how to optimize such delay while meeting a certain streaming rate requirement. We first formulate the minimum delay mesh problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge which serves as our benchmark and optimal solution for all the other schemes under comparison. Our heuristic makes use of the concept of power in network given by the ratio of throughput and delay. By maximizing the network power, our heuristic achieves very low delay. We then propose a simple distributed algorithm where peers select their parents based on the power concept. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation results show that our distributed protocol performs close to the centralized one, and substantially outperforms traditional and state-of-the-art approaches

A Simple FSPN Model of P2P Live Video Streaming System

Peer to Peer (P2P) live streaming is relatively new paradigm that aims at streaming live video to large number of clients at low cost. Many such applications already exist in the market, but, prior to creating such system it is necessary to analyze its performance via representative model that can provide good insight in the system’s behavior. Modeling and performance analysis of P2P live video streaming systems is challenging task which requires addressing many properties and issues of P2P systems that create complex combinatorial problem. Inspired by several related articles, in this paper we present our Fluid Stochastic Petri Net (FSPN) model for performance analysis of a mesh based P2P live video streaming system

An Economist's Guide to Digital Music

In this guide, we discuss the impact of digitalization on the music industry. We rely on market and survey data at the international level as well as expert statements from the industry. The guide investigates recent developments in legal and technological protection of digital music and describes new business models as well as consumers' attitude towards music downloads. We conclude the guide by a discussion of the evolution of the music industry

A Framework For Efficient Data Distribution In Peer-to-peer Networks.

Peer to Peer (P2P) models are based on user altruism, wherein a user shares its content with other users in the pool and it also has an interest in the content of the other nodes. Most P2P systems in their current form are not fair in terms of the content served by a peer and the service obtained from swarm. Most systems suffer from free rider\u27s problem where many high uplink capacity peers contribute much more than they should while many others get a free ride for downloading the content. This leaves high capacity nodes with very little or no motivation to contribute. Many times such resourceful nodes exit the swarm or don\u27t even participate. The whole scenario is unfavorable and disappointing for P2P networks in general, where participation is a must and a very important feature. As the number of users increases in the swarm, the swarm becomes robust and scalable. Other important issues in the present day P2P system are below optimal Quality of Service (QoS) in terms of download time, end-to-end latency and jitter rate, uplink utilization, excessive cross ISP traffic, security and cheating threats etc. These current day problems in P2P networks serve as a motivation for present work. To this end, we present an efficient data distribution framework in Peer-to-Peer (P2P) networks for media streaming and file sharing domain. The experiments with our model, an alliance based peering scheme for media streaming, show that such a scheme distributes data to the swarm members in a near-optimal way. Alliances are small groups of nodes that share data and other vital information for symbiotic association. We show that alliance formation is a loosely coupled and an effective way to organize the peers and our model maps to a small world network, which form efficient overlay structures and are robust to network perturbations such as churn. We present a comparative simulation based study of our model with CoolStreaming/DONet (a popular model) and present a quantitative performance evaluation. Simulation results show that our model scales well under varying workloads and conditions, delivers near optimal levels of QoS, reduces cross ISP traffic considerably and for most cases, performs at par or even better than Cool-Streaming/DONet. In the next phase of our work, we focussed on BitTorrent P2P model as it the most widely used file sharing protocol. Many studies in academia and industry have shown that though BitTorrent scales very well but is far from optimal in terms of fairness to end users, download time and uplink utilization. Furthermore, random peering and data distribution in such model lead to suboptimal performance. Lately, new breed of BitTorrent clients like BitTyrant have shown successful strategic attacks against BitTorrent. Strategic peers configure the BitTorrent client software such that for very less or no contribution, they can obtain good download speeds. Such strategic nodes exploit the altruism in the swarm and consume resources at the expense of other honest nodes and create an unfair swarm. More unfairness is generated in the swarm with the presence of heterogeneous bandwidth nodes. We investigate and propose a new token-based anti-strategic policy that could be used in BitTorrent to minimize the free-riding by strategic clients. We also proposed other policies against strategic attacks that include using a smart tracker that denies the request of strategic clients for peer listmultiple times, and black listing the non-behaving nodes that do not follow the protocol policies. These policies help to stop the strategic behavior of peers to a large extent and improve overall system performance. We also quantify and validate the benefits of using bandwidth peer matching policy. Our simulations results show that with the above proposed changes, uplink utilization and mean download time in BitTorrent network improves considerably. It leaves strategic clients with little or no incentive to behave greedily. This reduces free riding and creates fairer swarm with very little computational overhead. Finally, we show that our model is self healing model where user behavior changes from selfish to altruistic in the presence of the aforementioned policies