Performance evaluation of peer-assisted MPEG-DASH streaming in multi-user mobile scenarios
Performance evaluation of peer-assisted MPEG-DASH streaming with multiple concurrent users in mobile scenarios.
From Radio Telemetry to Perceptual Quality: A QoE-as-a-Service Framework for Multi-User OTT Streaming in 5G Vehicular Environments
A QoE-as-a-Service framework for 5G vehicular environments that maps radio telemetry to perceptual video quality metrics, enabling network-side QoE monitoring for OTT streaming …
SwarmLayer: A Hybrid P2P Assisted Solution for MPEG-DASH Streaming
SwarmLayer is a hybrid P2P layer for MPEG-DASH that uses WebRTC and Service Workers to offload up to 96% of server traffic with no changes to existing players.
A DASH server-side delay-based representation switching solution to improve the quality of experience for low-latency live video streaming
SSRS for LL-DASH live streaming uses server-measured client request delays to adapt video quality, achieving good quality-latency trade-offs without player modifications.
Performance Evaluation and Testbed for Delivering SRT Live Content using DASH Low Latency Streaming Systems
A testbed for evaluating LL-DASH live streaming from SRT/RTSP sources, achieving end-to-end delays under 2 seconds using FFmpeg and open-source players.
Mejora del streaming de vídeo en DASH con codificación de bitrate variable mediante el algoritmo Look Ahead y mecanismos de coordinación para la reproducción, y propuesta de nuevas métricas para la evaluación de la QoE
PhD thesis proposing the Look Ahead ABR algorithm for DASH, SAND-based multi-user coordination mechanisms, and VMAF-based QoE models for adaptive video streaming.
Transmisión inalámbrica multimedia coordinada con DASH-SAND
Sistema de streaming DASH coordinado mediante SAND que mejora la QoE en entornos inalámbricos con múltiples usuarios mediante intercambio de información de red.
New objective QoE models for evaluating ABR algorithms in DASH
Three new objective QoE models for DASH based on PSNR and VMAF, validated subjectively and via the Look Ahead ABR algorithm, outperforming existing models.
