Llama : Towards Low Latency Live Adaptive Streaming

Multimedia streaming, including on-demand and live delivery of content, has become the largest service, in terms of traffic volume, delivered over the Internet. The ever-increasing demand has led to remarkable advancements in multimedia delivery technology over the past three decades, facilitated by the concurrent pursuit of efficient and quality encoding of digital media. Today, the most prominent technology for online multimedia delivery is HTTP Adaptive Streaming (HAS), which utilises the stateless HTTP architecture - allowing for scalable streaming sessions that can be delivered to millions of viewers around the world using Content Delivery Networks. In HAS, the content is encoded at multiple encoding bitrates, and fragmented into segments of equal duration. The client simply fetches the consecutive segments from the server, at the desired encoding bitrate determined by an ABR algorithm which measures the network conditions and adjusts the bitrate accordingly. This method introduces new challenges to live streaming, where the content is generated in real-time, as it suffers from high end-to-end latency when compared to traditional broadcast methods due to the required buffering at client. This thesis aims to investigate low latency live adaptive streaming, focusing on the reduction of the end-to-end latency. We investigate the impact of latency on the performance of ABR algorithms in low latency scenarios by developing a simulation model and testing prominent on-demand adaptation solutions. Additionally, we conduct extensive subjective testing to further investigate the impact of bitrate changes on the perceived Quality of Experience (QoE) by users. Based on these investigations, we design an ABR algorithm suitable for low latency scenarios which can operate with a small client buffer. We evaluate the proposed low latency adaption solution against on-demand ABR algorithms and the state-of-the-art low latency ABR algorithms, under realistic network conditions using a variety of client and latency settings

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903