Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video最新文献

FALCON 猎鹰

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396931

Miguel Catalan-Cid, D. Camps-Mur, Mario Montagud, A. Betzler

引用次数: 2

Viewport prediction for 360° videos: a clustering approach 360°视频的视口预测:聚类方法

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396934

A. T. Nasrabadi, Aliehsan Samiei, R. Prakash

引用次数: 41

Evaluation of CMAF in live streaming scenarios 直播场景下CMAF的评估

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396932

Tomasz Lyko, M. Broadbent, N. Race, M. Nilsson, Paul Farrow, S. Appleby

{"title":"Evaluation of CMAF in live streaming scenarios","authors":"Tomasz Lyko, M. Broadbent, N. Race, M. Nilsson, Paul Farrow, S. Appleby","doi":"10.1145/3386290.3396932","DOIUrl":"https://doi.org/10.1145/3386290.3396932","url":null,"abstract":"HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces significant end to end latency compared to traditional broadcast, due to the the client requiring a large enough buffer for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by defining segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can significantly improve ABR performance in low delay scenarios.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Sensing multimedia contexts on mobile devices 在移动设备上感知多媒体上下文

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396935

M. A. Hoque, Ashwin Rao, Abhishek Kumar, M. Ammar, Pan Hui, S. Tarkoma

引用次数: 4

What you see is what you get: measure ABR video streaming QoE via on-device screen recording 你所看到的就是你得到的:测量ABR视频流QoE通过设备屏幕记录

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396938

Shichang Xu, E. Petajan, S. Sen, Z. Morley Mao

{"title":"What you see is what you get: measure ABR video streaming QoE via on-device screen recording","authors":"Shichang Xu, E. Petajan, S. Sen, Z. Morley Mao","doi":"10.1145/3386290.3396938","DOIUrl":"https://doi.org/10.1145/3386290.3396938","url":null,"abstract":"Analyzing delivered QoE for Adaptive Bitrate (ABR) streaming over cellular networks is critical for a host of entities including content providers and mobile network providers. However, existing approaches mostly rely on network traffic analysis. In addition to potential accuracy issues, they are challenged by the increasing use of end-to-end network traffic encryption. In this paper, we explore a very different approach to QoE measurement --- utilizing the screen recording capability widely available on commodity devices to record the video displayed on the mobile device screen, and analyzing the recorded video to measure the delivered QoE. We design a novel system VideoEye to conduct such screen-recording-based QoE analysis. We identify the various technical challenges involved, including distortions introduced by the screen recording process that can make such analysis difficult. We develop techniques to accurately measure video QoE from the screen recordings even in the presence of recording distortions. Our evaluations demonstrate that VideoEye accurately detects important QoE indicators including the track played at different points in time, and stall statistics. The maximal error in detected stall duration is 0.5 s. The accuracy of detecting the displayed tracks is higher than 97%.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127590990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

LiveClip: towards intelligent mobile short-form video streaming with deep reinforcement learning LiveClip:通过深度强化学习实现智能移动短视频流

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396937

Jian-Qian He, Miao Hu, Yipeng Zhou, Di Wu

{"title":"LiveClip: towards intelligent mobile short-form video streaming with deep reinforcement learning","authors":"Jian-Qian He, Miao Hu, Yipeng Zhou, Di Wu","doi":"10.1145/3386290.3396937","DOIUrl":"https://doi.org/10.1145/3386290.3396937","url":null,"abstract":"Recent years have witnessed great success of mobile short-form video apps. However, most current video streaming strategies are designed for long-form videos, which cannot be directly applied to short-form videos. Especially, short-form videos differ in many aspects, such as shorter video length, mobile friendliness, sharp popularity dynamics, and so on. Facing these challenges, in this paper, we perform an in-depth measurement study on Douyin, one of the most popular mobile short-form video platforms in China. The measurement study reveals that Douyin adopts a rather simple strategy (called Next-One strategy) based on HTTP progressive download, which uses a sliding window with stop-and-wait protocol. Such a strategy performs poorly when network connection is slow and user scrolling is fast. The results motivate us to design an intelligent adaptive streaming scheme for mobile short-form videos. We formulate the short-form video streaming problem and propose an adaptive short-form video streaming strategy called LiveClip using a deep reinforcement learning (DRL) approach. Trace-driven experimental results prove that LiveClip outperforms existing state-of-the-art approaches by around 10%-40% under various scenarios.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"38 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113992985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

PC-MCU: point cloud multipoint control unit for multi-user holoconferencing systems PC-MCU:多点云多点控制单元，用于多用户全息会议系统

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396936

G. Cernigliaro, Marc Martos Cabré, M. Montagud, A. Ansari, S. Fernández

{"title":"PC-MCU: point cloud multipoint control unit for multi-user holoconferencing systems","authors":"G. Cernigliaro, Marc Martos Cabré, M. Montagud, A. Ansari, S. Fernández","doi":"10.1145/3386290.3396936","DOIUrl":"https://doi.org/10.1145/3386290.3396936","url":null,"abstract":"This paper introduces the Point Cloud Multipoint Control Unit (PC-MCU): a key component for multi-user holoconferencing systems, where remote participants are represented as Point Clouds. The presented solution redefines the idea of MCU, broadly used to optimize connections and communications between users in traditional videoconferencing, and introduces a set of key features for the optimization of holoconferencing services where multiple users can be remotely connected. The PC-MCU is a virtualized cloud-based component, that aims at reducing the end-user client computational resources and bandwidth usage, providing the following key features: fusion of volumetric videos, Level of Detail (LoD) adjustment and non visible data removal. The results obtained for a scenario with two remote users, show how the introduction of the PC-MCU provides significant benefits in terms of computational resources and bandwidth savings, thus alleviating the requirements at the client side in holoconferencing services when compared to a baseline condition without using it. These improvements open the door to further research on this area to enable scalable and adaptive holoconferencing services using lightweight devices.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

SR360 SR360

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-06-08 DOI: 10.1145/3386290.3396929

Jiawen Chen, Miao Hu, Zhenxiao Luo, Zelong Wang, Di Wu

{"title":"SR360","authors":"Jiawen Chen, Miao Hu, Zhenxiao Luo, Zelong Wang, Di Wu","doi":"10.1145/3386290.3396929","DOIUrl":"https://doi.org/10.1145/3386290.3396929","url":null,"abstract":"360-degree videos have gained increasing popularity due to its capability to provide users with immersive viewing experience. Given the limited network bandwidth, it is a common approach to only stream video tiles in the user's Field-of-View (FoV) with high quality. However, it is difficult to perform accurate FoV prediction due to diverse user behaviors and time-varying network conditions. In this paper, we re-design the 360-degree video streaming systems by leveraging the technique of super-resolution (SR). The basic idea of our proposed SR360 framework is to utilize abundant computation resources on the user devices to trade off a reduction of network bandwidth. In the SR360 framework, a video tile with low resolution can be boosted to a video tile with high resolution using SR techniques at the client side. We adopt the theory of deep reinforcement learning (DRL) to make a set of decisions jointly, including user FoV prediction, bitrate allocation and SR enhancement. By conducting extensive trace-driven evaluations, we compare the performance of our proposed SR360 with other state-of-the-art methods and the results show that SR360 significantly outperforms other methods by at least 30% on average under different QoE metrics.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115049701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Self-play reinforcement learning for video transmission 视频传输的自播放强化学习

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-05-26 DOI: 10.1145/3386290.3396930

Tianchi Huang, Ruixiao Zhang, Lifeng Sun

{"title":"Self-play reinforcement learning for video transmission","authors":"Tianchi Huang, Ruixiao Zhang, Lifeng Sun","doi":"10.1145/3386290.3396930","DOIUrl":"https://doi.org/10.1145/3386290.3396930","url":null,"abstract":"Video transmission services adopt adaptive algorithms to ensure users' demands. Existing techniques are often optimized and evaluated by a function that linearly combines several weighted metrics. Nevertheless, we observe that the given function fails to describe the requirement accurately. Thus, such proposed methods might eventually violate the original needs. To eliminate this concern, we propose Zwei, a self-play reinforcement learning algorithm for video transmission tasks. Zwei aims to update the policy by straightforwardly utilizing the actual requirement. Technically, Zwei samples a number of trajectories from the same starting point, and instantly estimates the win rate w.r.t the competition outcome. Here the competition result represents which trajectory is closer to the assigned requirement. Subsequently, Zwei optimizes the strategy by maximizing the win rate. To build Zwei, we develop simulation environments, design adequate neural network models, and invent training methods for dealing with different requirements on various video transmission scenarios. Trace-driven analysis over two representative tasks demonstrates that Zwei optimizes itself according to the assigned requirement faithfully, outperforming the state-of-the-art methods under all considered scenarios.","PeriodicalId":402166,"journal":{"name":"Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Low-latency cloud-based volumetric video streaming using head motion prediction 使用头部运动预测的低延迟基于云的体积视频流

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2020-01-17 DOI: 10.1145/3386290.3396933

Serhan Gül, D. Podborski, T. Buchholz, T. Schierl, C. Hellge

引用次数: 34