ACM NOSSDAV’19: Bandwidth Prediction in Low-Latency Chunked Streaming

Bandwidth Prediction in Low-Latency Chunked Streaming

Abdelhak Bentaleb (National University of Singapore), Christian Timmerer (Alpen-Adria Universität & Bitmovin Inc,), Ali C. Begen (Ozyegin University), and Roger Zimmermann (National University of Singapore)

Abstract: HTTP adaptive streaming (HAS) with chunked transfer encoding can be used to reduce latency without sacrificing the coding efficiency. While this allows a media segment to be generated and delivered at the same time, it also causes grossly inaccurate bandwidth measurements, leading to incorrect bitrate selections. To overcome this effect, we design a novel Adaptive bitrate scheme for Chunked Transfer Encoding (ACTE) that leverages the unique nature of chunk downloads. It uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the available bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 64% reduction in stalls and a 27% increase in video quality.

Keywords: HAS; ABR; DASH; CMAF; low-latency; HTTP chunked transfer encoding; bandwidth measurement and prediction; RLS.

Acknowledgment: This research has been supported in part by the Singapore Ministry of Education Academic Research Fund Tier 1 under MOE’s official grant number T1 251RES1820 and the Austrian Research Promotion Agency (FFG) under the Next Generation Video Streaming project “PROMETHEUS”.

Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, and Roger Zimmermann. 2019. Bandwidth prediction in low-latency chunked streaming. In Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV ’19). ACM, New York, NY, USA, 7-13. DOI:

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