3,308 research outputs found
Price-based Controller for Quality-Fair HTTP Adaptive Streaming
HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair manner. However, rate fairness is in general not equivalent to quality fairness as different video sequences might have different characteristics and resource requirements. In this work, we focus on this limitation and propose a novel controller for HAS clients that is able to reach quality fairness while preserving the main characteristics of HAS systems and with a limited support from the network devices. In particular, we adopt a price-based mechanism in order to build a controller that maximizes the aggregate video quality for a set of HAS clients that share a common bottleneck. When network resources are scarce, the clients with simple video sequences reduce the requested bitrate in favor of users that subscribe to more complex video sequences, leading to a more efficient network usage. The proposed controller has been implemented in a network simulator, and the simulation results demonstrate its ability to share the available bandwidth among the HAS users in a quality-fair manner
Price-Based Controller for Utility-Aware HTTP Adaptive Streaming
HTTP Adaptive Streaming (HAS) permits to efficiently deliver video to multiple heterogenous
users in a fully distributed way. This might however lead to unfair bandwidth utilization among
HAS users. Therefore, network-assisted HAS systems have been proposed where network elements
operate alongside with the clients adaptation logic for improving users satisfaction. However,
current solutions rely on the assumption that network elements have full knowledge of the network
status, which is not always realistic. In this work, we rather propose a practical network-assisted
HAS system where the network elements infer the network link congestion using measurements
collected from the client endpoints, the congestion level signal is then used by the clients to
optimize their video data requests. Our novel controller maximizes the overall users satisfaction
and the clients share the available bandwidth fairly from a utility perspective, as demonstrated
by simulation results obtained on a network simulator
Optimized Adaptive Streaming Representations based on System Dynamics
Adaptive streaming addresses the increasing and heterogenous demand of
multimedia content over the Internet by offering several encoded versions for
each video sequence. Each version (or representation) has a different
resolution and bit rate, aimed at a specific set of users, like TV or mobile
phone clients. While most existing works on adaptive streaming deal with
effective playout-control strategies at the client side, we take in this paper
a providers' perspective and propose solutions to improve user satisfaction by
optimizing the encoding rates of the video sequences. We formulate an integer
linear program that maximizes users' average satisfaction, taking into account
the network dynamics, the video content information, and the user population
characteristics. The solution of the optimization is a set of encoding
parameters that permit to create different streams to robustly satisfy users'
requests over time. We simulate multiple adaptive streaming sessions
characterized by realistic network connections models, where the proposed
solution outperforms commonly used vendor recommendations, in terms of user
satisfaction but also in terms of fairness and outage probability. The
simulation results further show that video content information as well as
network constraints and users' statistics play a crucial role in selecting
proper encoding parameters to provide fairness a mong users and to reduce
network resource usage. We finally propose a few practical guidelines that can
be used to choose the encoding parameters based on the user base
characteristics, the network capacity and the type of video content
Online Resource Inference in Network Utility Maximization Problems
The amount of transmitted data in computer networks is expected to grow
considerably in the future, putting more and more pressure on the network
infrastructures. In order to guarantee a good service, it then becomes
fundamental to use the network resources efficiently. Network Utility
Maximization (NUM) provides a framework to optimize the rate allocation when
network resources are limited. Unfortunately, in the scenario where the amount
of available resources is not known a priori, classical NUM solving methods do
not offer a viable solution. To overcome this limitation we design an overlay
rate allocation scheme that attempts to infer the actual amount of available
network resources while coordinating the users rate allocation. Due to the
general and complex model assumed for the congestion measurements, a passive
learning of the available resources would not lead to satisfying performance.
The coordination scheme must then perform active learning in order to speed up
the resources estimation and quickly increase the system performance. By
adopting an optimal learning formulation we are able to balance the tradeoff
between an accurate estimation, and an effective resources exploitation in
order to maximize the long term quality of the service delivered to the users
Multimedia Social Networks: Game Theoretic Modeling and Equilibrium Analysis
Multimedia content sharing and distribution over multimedia social networks is more popular now than ever before: we download music from Napster, share our images on Flickr, view user-created video on YouTube, and watch peer-to-peer television using Coolstreaming, PPLive and PPStream. Within these multimedia social networks, users share, exchange, and compete for scarce resources such as multimedia data and bandwidth, and thus influence each other's decision and performance. Therefore, to provide fundamental guidelines for the better system design, it is important to analyze the users' behaviors and interactions in a multimedia social network, i.e., how users interact with and respond to each other.
Game theory is a mathematical tool that analyzes the strategic interactions among multiple decision makers. It is ideal and essential for studying, analyzing, and modeling the users' behaviors and interactions in social networking. In this thesis, game theory will be used to model users' behaviors in social networks and analyze the corresponding equilibria. Specifically, in this thesis, we first illustrate how to use game theory to analyze and model users' behaviors in multimedia social networks by discussing the following three different scenarios. In the first scenario, we consider a non-cooperative multimedia social network where users in the social network compete for the same resource. We use multiuser rate allocation social network as an example for this scenario. In the second scenario, we consider a cooperative multimedia social network where users in the social network cooperate with each other to obtain the content. We use cooperative peer-to-peer streaming social network as an example for this scenario. In the third scenario, we consider how to use the indirect reciprocity game to stimulate cooperation among users. We use the packet forwarding social network as an example.
Moreover, the concept of ``multimedia social networks" can be applied into the field of signal and image processing. If each pixel/sample is treated as a user, then the whole image/signal can be regarded as a multimedia social network. From such a perspective, we introduce a new paradigm for signal and image processing, and develop generalized and unified frameworks for classical signal and image problems. In this thesis, we use image denoising and image interpolation as examples to illustrate how to use game theory to re-formulate the classical signal and image processing problems
QoE-driven rate adaptation heuristic for fair adaptive video streaming
HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today's heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients' Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time
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