Tuesday, June 13, 2023

DASH-IF awarded Excellence in DASH award at ACM MMSys 2023

Vancouver, June 9, 2023

The DASH Industry Forum Excellence in DASH Award at ACM MMSys 2023 acknowledges papers substantially addressing MPEG-DASH as the presentation format and are selected for presentation at ACM MMSys 2023. Preference is given to practical enhancements and developments which can sustain future commercial usefulness of DASH. The DASH format used should conform to the DASH-IF Interoperability Points as defined by http://dashif.org/guidelines/. It is a financial prize as follows: first place – €750; second place – €500; and third place – €250. The winners are chosen by a DASH Industry Forum-appointed committee, and the results are final.

This year's award goes to the following papers:

First Place

Latency Target based Analysis of the DASH.js Player
Piers O’Hanlon and Adil Aslam

Abstract: We analyse the low latency performance of the three Adaptive Bitrate (ABR) algorithms in the dash.js Dynamic Adaptive Streaming over HTTP (DASH) player with respect to a range of latency targets and configuration options. We perform experiments on our DASH Testbed which allows for testing with a range of real world derived network profiles. Our experiments enable a better understanding of how latency targets affect quality of experience (QoE), and how well the different algorithms adhere to their targets. We find that with dash.js v4.5.0 the default Dynamic algorithm achieves the best overall QoE. We show that whilst the other algorithms can achieve higher video quality at lower latencies, they do so only at the expense of increased stalling. We analyse the poor performance of L2A-LL in our tests and develop modifications which demonstrate significant improvements. We also highlight how some low latency configuration settings can be detrimental to performance.

Second Place

Cross-layer Network Bandwidth Estimation for Low-latency Live ABR Streaming
Chinmaey Shende, Cheonjin Park, Subhabrata Sen, Bing Wang

Abstract: Low-latency live (LLL) adaptive bitrate (ABR) streaming relies critically on accurate bandwidth estimation to react to dynamic network conditions. While existing studies have proposed bandwidth estimation techniques for LLL streaming, these approaches are at the application level, and their accuracy is limited by the distorted timing information observed at the application level. In this paper, we propose a novel cross-layer approach that uses coarse-grained application-level semantics and fine-grained kernel-level packet capture to obtain accurate bandwidth estimation. We incorporate this technique in three popular open-source ABR players and show that it provides significantly more accurate bandwidth estimation than the state-of-the-art application-level approaches. In addition, the more accurate bandwidth estimation leads to better bandwidth prediction, which we show can lead to significantly better quality of experience (QoE) for end users.

Third Place

TASQ: Temporal Adaptive Streaming over QUIC
Akram Ansari, Yang Liu, Mea Wang, Emir Halepovic

Abstract: Traditional Adaptive BitRate (ABR) streaming faces a challenge of providing smooth experience under highly variable network conditions, especially when low latency is required. Effective adaptation techniques exist for deep-buffer scenarios, such as streaming long-form Video-on-Demand content, but remain elusive for short-form or low-latency cases, when even a short segment may be delivered too late and cause a stall. Recently proposed temporal adaptation aims to mitigate this problem by being robust to losing a part of the video segment, essentially dropping the tail of the segment intentionally to avoid the stall. In this paper, we analyze this approach in the context of a recently adopted codec AV1 and find that it does not always provide the promised benefits. We investigate the root causes and find that a combination of codec efficiency and TCP behavior can defeat the benefits of temporal adaptation. We develop a solution based on QUIC, and present the results showing that the benefits of temporal adaptation that still apply to AV1, including reduced stall time up to 65% compared to the original TCP-based approach. In addition, we present a novel way to use the stream management features of QUIC to benefit Quality-of-Experience (QoE) and reduce wasted data in video streaming.

The DASH-IF congratulates all winners and hopes to see you next year at ACM MMSys 2024.

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