Adaptive streaming addresses the increasing and heterogeneous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) is characterized by a resolution and a bit rate, and it is aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-buffer control strategies on the client side, in this article we take a providers' perspective and propose solutions to improve user satisfaction by optimizing the set of available representations. We formulate an integer linear program that maximizes users' average satisfaction, taking into account network dynamics, type of video content, and user population characteristics. The solution of the optimization is a set of encoding parameters corresponding to the representations set that maximizes user satisfaction. We evaluate this solution by simulating multiple adaptive streaming sessions characterized by realistic network statistics, showing that 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 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 among users and to reduce network resource usage. We finally propose a few theoretical guidelines that can be used, in realistic settings, to choose the encoding parameters based on the user characteristics, the network capacity and the type of video content.
During the last decade, adaptive streaming has been an active research area, with most efforts aimed at developing server-controlled streaming solutions. Recently, a client-driven approach, based on HTTP-adaptive streaming @cite_7 @cite_1 , has gained popularity and attention. In this new paradigm, the clients decide which segments to get and when to request them, and the server mainly responds to the clients' requests. Different implementation of this new architecture have been proposed in various commercial DASH players @cite_10 .
@cite_10 @cite_1 @cite_7
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Adaptive (video) streaming over HTTP is gradually being adopted by content and network service providers, as it offers significant advantages in terms of both user-perceived quality and resource utilization. In this paper, we first focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming and Netflix) and one open-source player (Adobe's OSMF). We first examine how the previous three players react to persistent and short-term changes in the underlying network available bandwidth. Do they quickly converge to the maximum sustainable bitrate? We identify major differences between the three players and significant inefficiencies in each of them. We then propose a new adaptation algorithm, referred to as AdapTech Streaming, which aims to address the problems with the previous three players. In the second part of the paper, we consider the following two questions. First, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share that resource in a stable and fair manner? And second, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay, keeping the viewing experience ''live''? MPEG has recently finalized a new standard to enable dynamic and adaptive streaming of media over HTTP. This standard aims to address the interoperability needs between devices and servers of various vendors. There is broad industry support for this new standard, which offers the promise of transforming the media-streaming landscape. In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and examples are provided.