"This award is presented every year, starting in 2020, to the authors of the paper published either 10, 11 or 12 years previously at an SIGMM sponsored or co-sponsored conference (so the 2020 award would be for papers at a 2008, 2009 or 2010 SIGMM conference). The award recognizes the paper that has had the most impact and influence on the field of Multimedia in terms of research, development, product or ideas, during the intervening years, as selected by a selection committee. The contributions the selection committee will focus on may be theoretical advances, techniques and/or software tools that have been widely used, and/or innovative applications that have had impact on multimedia computing."
Interestingly, in the past three years, papers related to MPEG-DASH were always among the winners or honorable mentions as follows:
2021 AwardsWinner (MM Systems & Networking)
Thomas Stockhammer. 2011. Dynamic adaptive streaming over HTTP --: standards and design principles. In Proceedings of the second annual ACM conference on Multimedia systems (MMSys '11). Association for Computing Machinery, New York, NY, USA, 133–144. https://dl.acm.org/doi/10.1145/1943552.1943572
Abstract: 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.
2022 AwardsHonorable Mention, in the category of “Multimedia Systems- Networks”
Saamer Akhshabi, Ali C. Begen, and Constantine Dovrolis. 2011. An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In Proceedings of the second annual ACM conference on Multimedia systems (MMSys '11). Association for Computing Machinery, New York, NY, USA, 157–168. https://dl.acm.org/doi/10.1145/1943552.1943574
Abstract: Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.
2023 AwardsHonorable Mention, in the category of "MM Systems & Networking"
Stefan Lederer, Christopher Müller, and Christian Timmerer. 2012. Dynamic adaptive streaming over HTTP dataset. In Proceedings of the 3rd Multimedia Systems Conference (MMSys '12). Association for Computing Machinery, New York, NY, USA, 89–94. https://dl.acm.org/doi/10.1145/2155555.2155570
Abstract: The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.