Adaptive bitrate streaming is a technique used in streaming multimedia over computer networks. While in the past most video or audio streaming technologies utilized streaming protocols such as RTP with RTSP, today's adaptive streaming technologies are based almost exclusively on HTTP, and are designed to work efficiently over large distributed HTTP networks. Adaptive bitrate streaming works by detecting a user's bandwidth and CPU capacity in real time, adjusting the quality of the media stream accordingly. It requires the use of an encoder which encodes a single source media (video or audio) at multiple bit rates. The player client switches between streaming the different encodings depending on available resources. "The result: very little buffering, fast start time and a good experience for both high-end and low-end connections."
More specifically, adaptive bitrate streaming is a method of video streaming over HTTP where the source content is encoded at multiple bit rates. Each of the different bit rate streams are segmented into small multi-second parts. The segment size can vary depending on the particular implementation, but they are typically between two and ten seconds. First, the client downloads a that describes the available stream segments and their respective bit rates. During stream start-up, the client usually requests the segments from the lowest bit rate stream. If the client finds that the network throughput is greater than the bit rate of the downloaded segment, then it will request a higher bit rate segment. Later, if the client finds that the network throughput has deteriorated, it will request a lower bit rate segment. An adaptive bitrate (ABR) algorithm in the client performs the key function of deciding which bit rate segments to download, based on the current state of the network. Several types of ABR algorithms are in commercial use: throughput-based algorithms use the throughput achieved in recent prior downloads for decision-making (e.g., throughput rule in dash.
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The Real-time Transport Protocol (RTP) is a network protocol for delivering audio and video over IP networks. RTP is used in communication and entertainment systems that involve streaming media, such as telephony, video teleconference applications including WebRTC, television services and web-based push-to-talk features. RTP typically runs over User Datagram Protocol (UDP). RTP is used in conjunction with the RTP Control Protocol (RTCP). While RTP carries the media streams (e.g.
Internet Protocol television (IPTV) is the delivery of television content over Internet Protocol (IP) networks. This is in contrast to delivery through traditional terrestrial, satellite, and cable television formats. Unlike downloaded media, IPTV offers the ability to stream the source media continuously. As a result, a client media player can begin playing the content (such as a TV channel) almost immediately. This is known as streaming media. Although IPTV uses the Internet protocol it is not limited to television streamed from the Internet (Internet television).
Streaming media is multimedia that is delivered and consumed in a continuous manner from a source, with little or no intermediate storage in network elements. Streaming refers to the delivery method of content, rather than the content itself. Distinguishing delivery method from the media applies specifically to telecommunications networks, as most of the traditional media delivery systems are either inherently streaming (e.g. radio, television) or inherently non-streaming (e.g. books, videotapes, audio CDs).
In this course we will define rigorous mathematical models for computing on large datasets, cover main algorithmic techniques that have been developed for sublinear (e.g. faster than linear time) data
This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up
prototypes to Spark clusters. It exposes the students to the entire data science pipe
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Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...
We introduce a new class of succinct arguments, that we call elastic. Elastic SNARKs allow the prover to allocate different resources (such as memory and time) depending on the execution environment and the statement to prove. The resulting output is indep ...
SPRINGER INTERNATIONAL PUBLISHING AG2022
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To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...