QoE-Based Low-Delay Live Streaming Using Throughput Predictions

MILLER, Konstantin, AL TAMIMI, Abdel-Karim and WOLISZ, Adam (2017). QoE-Based Low-Delay Live Streaming Using Throughput Predictions. ACM Transactions on Multimedia Computing, Communications, and Applications, 13 (1), 1-24.

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Official URL: https://dl.acm.org/doi/10.1145/2990505
Link to published version:: https://doi.org/10.1145/2990505
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Recently, Hypertext Transfer Protocol (HTTP)-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to the varying network conditions to ensure a high quality of experience (QoE)—that is, minimize playback interruptions while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless access network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 20 to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (short for low-latency prediction-based adaptation), which is designed to operate with a transport latency of a few seconds. To reach this goal, LOLYPOP leverages Transmission Control Protocol throughput predictions on multiple time scales, from 1 to 10 seconds, along with estimations of the relative prediction error distributions. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the QoE by maximizing the average video quality as a function of the number of skipped segments and quality transitions. To select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions, limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm called FESTIVE. We observed that the average selected video representation index is by up to a factor of 3 higher than with the baseline approach. We also observed that LOLYPOP is able to reach points from a broader region in the QoE space, and thus it is better adjustable to the user profile or service provider requirements.

Item Type: Article
Uncontrolled Keywords: Adaptive streaming; MPEG-DASH; TCP throughput prediction; 0803 Computer Software; 0805 Distributed Computing; 0806 Information Systems; Artificial Intelligence & Image Processing
Identification Number: https://doi.org/10.1145/2990505
Page Range: 1-24
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 27 Apr 2023 10:04
Last Modified: 11 Oct 2023 15:45
URI: https://shura.shu.ac.uk/id/eprint/31053

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