Adaptive Quality and Energy Enhancement in Video Streaming with RecABR
ACM 4th Mile-High Video Conference (MHV’25)
18–20 February 2025 | Denver, CO, USA
[PDF]
Daniele Lorenzi (Alpen-Adria Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria Universität Klagenfurt, Austria)
Abstract: HTTP Adaptive Streaming (HAS) dominates video delivery but faces sustainability issues due to its energy demands. Current adaptive bitrate (ABR) algorithms prioritize quality, neglecting the energy costs of higher bitrates. Super-resolution (SR) can enhance quality but increases energy use, especially for GPU-equipped devices in competitive networks. RecABR addresses these challenges by clustering clients based on device attributes (e.g., GPU, resolution) and optimizing parameters via linear programming. This reduces computational overhead and ensures energy-efficient, quality-aware recommendations. Using metrics like VMAF and compressed SR models, RecABR minimizes storage and processing costs, making it scalable for CDN edge deployment.
Index Terms— QoE, HAS, Super-resolution, Energy.
