{"id":11255,"date":"2017-01-17T08:36:21","date_gmt":"2017-01-17T08:36:21","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=11255"},"modified":"2017-01-17T10:08:49","modified_gmt":"2017-01-17T10:08:49","slug":"importance-beating-buffering","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/importance-beating-buffering\/","title":{"rendered":"The importance of beating buffering"},"content":{"rendered":"

Streaming video over mobile networks could be greatly improved by smart prioritization of visually important data.<\/strong><\/em><\/span><\/p>\n

\"\"
A*STAR researchers have developed a video Quality of Experience (QoE) scheme that can greatly improve streaming video in crowded mobile environments.
\u00a9 Hero Images\/Getty<\/figcaption><\/figure>\n

By prioritizing the delivery of rich visual data, A*STAR researchers have demonstrated that the quality of streaming video can be vastly improved on even the most crowded wireless networks.<\/span><\/p>\n

Video streaming is one of the most demanding tasks on mobile networks, not only because of the large amount of data that needs to be transmitted, but because even the faintest stutter or artifact in video playback can dramatically degrade the experience. Network engineers are continuously looking for new ways to maximize video quality in increasingly congested wireless environments with many users vying for limited bandwidth.<\/span><\/p>\n

There are already methods for guaranteeing a certain transmission rate to maintain the quality of streaming video and audio. Known as Quality of Service (QoS) protocols, these methods work well in many cases, but generally require a large allocation of bandwidth to each user, which might not be available on crowded mobile networks. Peng Hui Tan, Maodong Li and colleagues from the A*STAR Institute for Infocomm Research instead studied how it might be possible to rate the importance of discrete video \u2018packets\u2019 to reduce the bandwidth needed to maintain a certain Quality of Experience, or QoE.<\/span><\/p>\n

\u201cQoE refers to the performance metric used to gauge the experience of the end user,\u201d explains Tan. \u201cWe need to translate a given QoE into a set of parameters for QoS, which is then implemented in the network communication protocol. We found that by passing information across the different layers of communications, from video playback application to network transmission, we could enhance the QoE through more efficient allocation of network resources.\u201d<\/span><\/p>\n

The researchers developed an efficient method to derive an \u2018importance index\u2019 for each video packet based on the video bit rate, which varies packet-to-packet depending on how much new information needs to be displayed \u2013 for example slow scenes with little movement require lower bit rates, while fast action scenes require very high bit rates.<\/span><\/p>\n

By prioritizing video packets \u2013 each a fraction of an individual frame of video \u2013 based on bit rate and other network parameters, then inserting this priority in the QoS scheme in real time, the team was able to achieve a significant enhancement in the perceived quality of streaming video among multiple users in a laboratory environment with limited wireless bandwidth.<\/span><\/p>\n

\u201cFor the end user, video quality will be improved with less distortion, while service providers can accommodate more users with the same network resources,\u201d says Tan.<\/span><\/p>\n

The A*STAR-affiliated researchers contributing to this research are from the Institute for Infocomm Research.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Streaming video over mobile networks could be greatly improved by smart prioritization of visually important data. By prioritizing the delivery of rich visual data, A*STAR researchers have demonstrated that the quality of streaming video can be vastly improved on even the most crowded wireless networks. Video streaming is one of the most demanding tasks on […]<\/p>\n","protected":false},"author":6,"featured_media":11256,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[43,22],"tags":[],"class_list":["post-11255","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-computer-science","category-other"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea-300x200.jpg",300,200,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",95,63,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",300,200,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2017\/01\/researchsea.jpg",150,100,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Computer Science<\/a> Other<\/a>","tag_info":"Other","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/11255","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=11255"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/11255\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/11256"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=11255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=11255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=11255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}