Rest Overview

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Tecnología

REST For Web Services
Course, Fall 2005

Henning Niss

IT University of Copenhagen

Lecture 7

Henning Niss (ITU) REST Lecture 7 1 / 34

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Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.

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Abstract: In today’s dynamic streaming landscape, where viewers access content on various devices and encounter fluctuating network conditions, optimizing video delivery for each unique scenario is imperative. Video content complexity analysis, content-adaptive video coding, and multi-encoding methods are fundamental for the success of adaptive video streaming, as they serve crucial roles in delivering high-quality video experiences to a diverse audience. Video content complexity analysis allows us to comprehend the video content’s intricacies, such as motion, texture, and detail, providing valuable insights to enhance encoding decisions. Content-adaptive video coding techniques built upon this analysis involve dynamically adjusting encoding parameters based on the content complexity. This adaptability ensures that the video stream remains visually appealing and artifacts are minimized, even under challenging network conditions. Multi-encoding methods further bolster adaptive streaming by offering faster encoding of multiple representations of the same video at different bitrates, which reduces computational overhead and enables efficient resource allocation on the server side. In this light, this dissertation proposes contributions categorized into four classes: Video complexity analysis: This thesis proposes discrete cosine transform (DCT)-energy-based spatial and temporal complexity features to overcome the limitations of state-of-the-art spatiotemporal complexity features and provide an efficient video complexity analysis regarding accuracy and speed for every video (segment). Content-adaptive encoding optimizations: This thesis proposes a scene detection algorithm using the video complexity analysis features. Moreover, an intra coding unit depth prediction algorithm is proposed, which limits rate-distortion optimization for each coding tree unit in high efficiency video coding by utilizing the spatial correlation with the neighboring CTUs. Online per-title encoding optimizations: This thesis proposes dynamic resolution prediction, dynamic framerate prediction, perceptually-aware bitrate ladder prediction, and dynamic encoding preset prediction schemes to advance the landscape of adaptive streaming. By addressing these critical aspects, the research establishes adaptive streaming systems that offer superior quality, efficiency, and user engagement, paving the way for a new era of tailored multimedia experience. Multi-encoding optimizations: This dissertation comprehensively proposes various multi-rate and multi-encoding schemes in serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the rate-distortion optimization (RDO) process across multiple representations.

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