Real-Time MPC for Adaptive Video Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is the standard for video streaming applications such as YouTube and Netflix. According to DASH, the video player must include an Adaptive Bit-Rate (ABR) controller designed to maximize users' Quality of Experience (QoE). The controller must avoid playback interruptions, due to buffer underflow, while at the same time selecting the maximum video encoding quality compatible with the available time-varying bandwidth. This paper proposes a controller designed using a nicely constrained Model Predictive Control (MPC) which employs Bellman Dynamic Programming (DP) to reduce the computational cost of the algorithm from exponential to polynomial. Compared with state-of-the-art ABR algorithms, the proposed Real-Time MPC (RT-MPC) improves QoE while remarkably reducing the computational time, so that the algorithm can be used for both live-video streaming and real-time conferencing.

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Publication Details

Published
2025-01-10
DOI
https://doi.org/10.1109/ccnc54725.2025.10976087
Citations
2
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
2.97
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article

Real-Time MPC for Adaptive Video Streaming

Saverio Mascolo, Gioacchino Manfredi, Luca De Cicco, Vito Andrea Racanelli
2 citations
Advanced Data Storage Technologies
2.97
article

Real-Time MPC for Adaptive Video Streaming

Saverio Mascolo, Gioacchino Manfredi, Luca De Cicco, Vito Andrea Racanelli
article en
2 citations

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is the standard for video streaming applications such as YouTube and Netflix. According to DASH, the video player must include an Adaptive Bit-Rate (ABR) controller designed to maximize users' Quality of Experience (QoE). The controller must avoid playback interruptions, due to buffer underflow, while at the same time selecting the maximum video encoding quality compatible with the available time-varying bandwidth. This paper proposes a controller designed using a nicely constrained Model Predictive Control (MPC) which employs Bellman Dynamic Programming (DP) to reduce the computational cost of the algorithm from exponential to polynomial. Compared with state-of-the-art ABR algorithms, the proposed Real-Time MPC (RT-MPC) improves QoE while remarkably reducing the computational time, so that the algorithm can be used for both live-video streaming and real-time conferencing.

Polytechnic University of Bari (IT)
Affordable and clean energy
Openalex Percentile: Top 9%
Advanced Data Storage Technologies
2.97
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