Open-source Player Research Articles

A Practical Evaluation of Rate Adaptation Algorithms in HTTP-based Adaptive Streaming

The HTTP-based Adaptive Streaming (HAS) techniques are widely used in Internet video streaming services, including YouTube and Netflix. The Dynamic Adaptive Streaming over HTTP (DASH) is the latest international standard that facilitates the interoperability of different HAS techniques of various vendors. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. The rate adaptation algorithms, however, are not part of the standard, and the details of the algorithms are left to vendors. As a result, there are many different algorithms adopted in both commercial and open source players while the detailed algorithms and their performance are barely understood. In this paper, we investigate the detailed operations of the different players by code level analysis and through reverse engineering. Specifically, we present the pseudo codes of 3 open source players and devise a method to obtain the detailed operation information, e.g., bitrate and buffer amount, of popular streaming players whose source codes are not publicly available. We conduct extensive experiments on our testbed and provide suggestions based on the behaviors of these players, including the repeated over-estimation of the available bandwidth, unfair bitrate selection when multiple players compete for the bandwidth, and insensitivity of Quick UDP Internet Connections (QUIC) protocol to the varying network bandwidth.

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

BackgroundThere has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software.ResultsThe Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery.ConclusionOpen Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT’s source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0078-2) contains supplementary material, which is available to authorized users.

An experimental evaluation of rate-adaptive video players over HTTP

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”?