[발표] 2015개정 실과 교육과정 ‘가정생활’ 영역 개발 방향

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وضعیت برونداد علمی دانشگاه علوم پزشکی کردستان براساس شاخصهای علم سنجی تا پایان سال 2011 میلادی

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Клініко-епідеміологічні особливості спалаху кору в м. Суми у 2014 році

It is highly challenging to simultaneously achieve high-rate and low-latency in live video streaming. Chunk-based streaming and playback speed adaptation are two promising new trends to achieve high user Quality-of-Experience (QoE). To thoroughly understand their potentials, we develop a detailed chunk-level dynamic model that characterizes how video rate and playback speed jointly control the evolution of a live streaming session. Leveraging on the model, we first study the optimal joint video rate-playback speed adaptation as a non-linear optimal control problem. We further develop model-free joint adaptation strategies using deep reinforcement learning. Through extensive experiments, we demonstrate that our proposed joint adaptation algorithms significantly outperform rate-only adaptation algorithms and the recently proposed low-latency video streaming algorithms that separately adapt video rate and playback speed without joint optimization. In a wide-range of network conditions, the model-based and model-free algorithms can achieve close-to-optimal trade-offs tailored for users with different QoE preferences.

Tightrope walking in low-latency live streaming: optimal joint adaptation of video rate and playback speed

Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hard-coded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL’s wide deployment. Second, LoL’s objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , which has been integrated to the official dash.js player in v3.2.0. 

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Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Achieving low-latency is paramount for live streaming scenarios, that are now-days becoming increasingly popular. In this paper, we propose a novel algorithm for bitrate adaptation in HTTP Adaptive Streaming (HAS), based on Online Convex Optimization (OCO). The proposed algorithm, named Learn2Adapt-LowLatency (L2A-LL), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, throughput estimation or application-specific adjustments. These properties make it very suitable for users who typically experience fast variations in channel characteristics. The proposed algorithm has been implemented in DASH-IF's reference video player (dash.js) and has been made publicly available for research purposes at [22]. Real experiments show that L2A-LL reduces latency significantly, while providing a high average streaming bit-rate, without impairing the overall Quality of Experience (QoE); a result that is independent of the channel and application scenarios. The presented optimization framework, is robust due to its design principle; its ability to learn and allows for modular QoE prioritization, while it facilitates easy adjustments to consider applications beyond live streaming and/or multiple user classes.

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Online learning for low-latency adaptive streaming

Real-time Distributed Media Processing Workflows (DMPW) are popular for online media delivery. Combining distributed media sources and processing can reduce storage costs and increase flexibility. However, high request rates may result in unacceptable latency or even failures in incorrect configurations. Thus, testing DMPW deployments at scale is key, particularly for real-time cases. We propose the new MPEG Network Based Media Processing (NBMP) standard for this and present a testbed implementation that includes all the reference components. In addition, the testbed includes a set of configurable functions for load generation, monitoring, data-collection and visualization. The testbed is used to test Dynamic Adaptive HTTP streaming functions under different workloads in a standardized and reproducible manner. A total of 327 tests with different loads and Real-Time DMPW configurations were completed. The results provide insights in the performance, reliability and time-consistency of each configuration. Based on these tests, we selected the preferred cloud instance type, considering hypervisor options and different function implementation configurations. Further, we analyzed different processing tasks and options for distributed deployments on edge and centralized clouds. Last, a classifier was developed to detect if failures happen under a certain workload. Results also show that, normalized inter-experiment standard deviation of the metric means can be an indicator for unstable or incorrect configurations.

MPEG NBMP testbed for evaluation of real-time distributed media processing workflows at scale

HTTP Adaptive Streaming (HAS) is increasingly deployed at large, gradually replacing traditional broadcast. However, testing large-scale deployments remains challenging, costly and error-prone. Especially, testing with realistic streaming loads from massive numbers of users is challenging and costly. To improve this, we introduce an open-source load testing tool that can be deployed in the cloud or on-premise in a distributed manner, for load generation.Our presented tool is an extension of an existing open-source web-application load-testing tool. In particular we have added functionality, that includes streaming load generation for a multitude of protocols (i.e. Dynamic Adaptive Streaming over HTTP (DASH) and HTTP-Live-Streaming (HLS)) and use-case implementations (e.g. live streaming, Video on Demand (VoD), bit-rate switching). The extension facilitates testing streaming back-ends at scale in a resource-efficient manner. We illustrate our tool's capabilities via a series of use-cases, designed to test, among others, how streaming deployments perform under different load scenarios, i.e. steep or gradual user ramp-up and stability testing over long periods.

A scalable load generation framework for evaluation of video streaming workflows in the cloud

In this paper, we propose a novel algorithm for video bitrate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust bitrate adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Experimental results, over real 4G traffic traces, show that L2A improves on the overall Quality of Experience (QoE) and in particular the average streaming bitrate, a result obtained independently of the channel and application scenarios.

Theo Karagkioules

Papers

Online learning for low-latency adaptive streaming

MPEG NBMP testbed for evaluation of real-time distributed media processing workflows at scale

A scalable load generation framework for evaluation of video streaming workflows in the cloud

Online Learning for Adaptive Video Streaming in Mobile Networks