Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video最新文献

CAVE 洞穴

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534350

A. Ali-Eldin, Chirag Goel, M. Jha, Bo Chen, Klara Nahrstedt, P. Shenoy

{"title":"CAVE","authors":"A. Ali-Eldin, Chirag Goel, M. Jha, Bo Chen, Klara Nahrstedt, P. Shenoy","doi":"10.1145/3534088.3534350","DOIUrl":"https://doi.org/10.1145/3534088.3534350","url":null,"abstract":"While 360° videos are gaining popularity due to the emergence of VR technologies, storing and streaming such videos can incur up to 20X higher overheads than traditional HD content. Edge caching, which involves caching and serving 360° videos from edge servers, is one possible approach for addressing these overheads. Prior work on 360° video caching has been based on using past history to cache tiles that are likely to be in a viewer's field of view and has not considered methods to intelligently share a limited edge cache across a set of videos that exhibit large variations in their popularity, size, content, and user abandonment patterns. Towards this end, we present CAVE, an adaptive edge caching framework that intelligently optimizes cache allocation across a set of videos taking into account video content, size, and popularity. Our experiments using realistic video workloads shows CAVE improves cache hit-rates, and thus network saving, by up to 50% over state-of-the-art approaches, while also scaling to up to two thousand videos per edge cache. In addition, in terms of scalability, our developed algorithm is embarrassingly parallel, allowing CAVE to scale beyond state-of-the-art solutions that typically do not support parallelization.","PeriodicalId":150454,"journal":{"name":"Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122090732","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: 0

PRIOR: deep reinforced adaptive video streaming with attention-based throughput prediction 先验:深度增强自适应视频流与基于注意力的吞吐量预测

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534348

Danfu Yuan, Yuanhong Zhang, Weizhan Zhang, Xuncheng Liu, Haipeng Du, Qinghua Zheng

{"title":"PRIOR: deep reinforced adaptive video streaming with attention-based throughput prediction","authors":"Danfu Yuan, Yuanhong Zhang, Weizhan Zhang, Xuncheng Liu, Haipeng Du, Qinghua Zheng","doi":"10.1145/3534088.3534348","DOIUrl":"https://doi.org/10.1145/3534088.3534348","url":null,"abstract":"Video service providers have deployed dynamic video bitrate adaptation services to fulfill user demands for higher video quality. However, fluctuations and instability of network conditions inhibit the performance promotion of adaptive bitrate (ABR) algorithms. Existing rule-based approaches fail to guarantee accurate throughput estimates, and learning-based algorithms are considerably sensitive to the variability of network. Therefore, how to gain effective and stable throughput estimates has become one of the critical challenges to further enhancing ABR methods. To eliminate this concern, we propose PRIOR, an ABR algorithm that fuses an effective throughput prediction module and a state-of-the-art multi-agent reinforcement learning method to provide a high quality of experience (QoE). PRIOR aims to maximize the QoE metric by straightforwardly utilizing accurate throughput estimates rather than past throughput measurements. Specifically, PRIOR employs a light-weighted prediction module with attention mechanism to obtain effective future throughput. Considering the excellent features introduced by the HTTP/3 protocol, we apply PRIOR to trace-driven simulations and real-world scenarios over HTTP/1.1 and HTTP/3. Trace-driven emulation illustrates that PRIOR outperforms existing ABR schemes over HTTP/1.1 and HTTP/3, and our prediction module can also reinforce the performance of other ABR algorithms. Extensive results on real-world evaluation demonstrate the superiority of PRIOR over existing state-of-the-art ABR schemes.","PeriodicalId":150454,"journal":{"name":"Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123964871","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

FLAD: a human-centered video content flaw detection system for meeting recordings FLAD:以人为本的会议录像视频内容缺陷检测系统

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534349

Haihan Duan, Junhua Liao, Lehao Lin, Wei Cai

引用次数: 1

Geometry-guided compact compression for light field image using graph convolutional networks 基于图形卷积网络的光场图像几何导向压缩

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534345

Yu Liu, Linhui Wei, Heming Zhao, Jingming Shan, Yumei Wang

{"title":"Geometry-guided compact compression for light field image using graph convolutional networks","authors":"Yu Liu, Linhui Wei, Heming Zhao, Jingming Shan, Yumei Wang","doi":"10.1145/3534088.3534345","DOIUrl":"https://doi.org/10.1145/3534088.3534345","url":null,"abstract":"Light field records the information of the light in space and contributes to regenerate the content effectively, which makes immersive media more promising. In this paper, we propose a geometry-guided compact compression scheme (GCC) for light field image. We regard that the geometry of GCC includes the structure in a single sub-aperture image (SAI) and the relationship among SAIs, which can be used to fully explore the compact representation for light field image. The light field image is grouped into key SAIs and non-key SAIs. The key SAIs are obtained by down-sampling in the angular domain and arranged into the pseudo-sequence that needs to be compressed. We consider the superpixel-based segmentation algorithm to detect the contours and obtain the sketch map for the non-key SAIs. Meanwhile, the graph model is used to establish the relationships among the SAIs by the vertices and edges. On the decoder side, the light field image is reconstructed by the graph convolutional networks, and the sketch map optimizes the details of the recovered images to some extent. Experimental results show the benefit of GCC in terms of rate-distortion performances compare with several state-of-the-art methods for the real-world and synthetic light field datasets. Besides, the proposed GCC is able to generalize over datasets not seen during training.","PeriodicalId":150454,"journal":{"name":"Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114103098","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: 1

FLAD

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1201/b18854-142

Haihan Duan, Junhua Liao, Lehao Lin, Wei Cai

引用次数: 0

Measurement of the responses of cloud-based game streaming to network congestion 基于云的游戏流对网络拥塞的响应测量

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534346

Xiaokun Xu, M. Claypool

引用次数: 2

Revisiting super-resolution for internet video streaming 重新审视互联网视频流的超分辨率

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534344

Zelong Wang, Zhenxiao Luo, Miao Hu, Di Wu, Youlong Cao, Yi Qin

{"title":"Revisiting super-resolution for internet video streaming","authors":"Zelong Wang, Zhenxiao Luo, Miao Hu, Di Wu, Youlong Cao, Yi Qin","doi":"10.1145/3534088.3534344","DOIUrl":"https://doi.org/10.1145/3534088.3534344","url":null,"abstract":"Recent advancements of neural-enhanced techniques, especially super-resolution (SR), show great potential in revolutionizing the landscape of Internet video delivery. However, there are still quite a few key questions (e.g., how to choose a proper resolution configuration for training samples, how to set the training patch size, how to perform the best patch selection, how to set the update frequency of SR model) that have not been well investigated and understood. In this paper, we perform a dedicated measurement study to revisit super-resolution techniques for Internet video streaming. Our measurements are based on real-world video datasets, and the results provide a number of important insights: (1) It is possible that the SR model trained with low-resolution patches (e.g., (540p, 1080p) pairs) can achieve almost the same performance as that trained with high-resolution patches (e.g., (1080p, 2160p) pairs); (2) Compared to the saliency of training patches, the size of training patches has little impact on the performance of trained SR model; (3) The improvement of video quality brought by more frequent SR model update is not very significant. We also discuss the implications of our findings for system design, and we believe that our work is essential for paving the way for the success of future neural-enhanced video streaming systems.","PeriodicalId":150454,"journal":{"name":"Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131936743","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: 6

Dynamic DNN model selection and inference off loading for video analytics with edge-cloud collaboration 边缘云协作视频分析的动态DNN模型选择和推理卸载

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534352

Xuezhi Wang, Guanyu Gao, Xiaohu Wu, Yan Lyu, Weiwei Wu

引用次数: 5

Preserving privacy in mobile spatial computing 移动空间计算中的隐私保护

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534343

Nan Wu, Ruizhi Cheng, Songqing Chen, Bo Han

引用次数: 0

Power-efficient live virtual reality streaming using edge offloading 使用边缘卸载的高能效实时虚拟现实流

Proceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video Pub Date : 2022-06-17 DOI: 10.1145/3534088.3534351

Ziehen Zhu, Xianglong Feng, Zhongze Tang, Nan Jiang, Tian Guo, Lisong Xu, Sheng Wei

引用次数: 1