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[Keyword] MPEG-DASH(3hit)

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  • A Low-Latency 4K HEVC Multi-Channel Encoding System with Content-Aware Bitrate Control for Live Streaming

    Daisuke KOBAYASHI  Ken NAKAMURA  Masaki KITAHARA  Tatsuya OSAWA  Yuya OMORI  Takayuki ONISHI  Hiroe IWASAKI  

     
    PAPER-Image Processing and Video Processing

      Pubricized:
    2022/09/30
      Vol:
    E106-D No:1
      Page(s):
    46-57

    This paper describes a novel low-latency 4K 60 fps HEVC (high efficiency video coding)/H.265 multi-channel encoding system with content-aware bitrate control for live streaming. Adaptive bitrate (ABR) streaming techniques, such as MPEG-DASH (dynamic adaptive streaming over HTTP) and HLS (HTTP live streaming), spread widely on Internet video streaming. Live content has increased with the expansion of streaming services, which has led to demands for traffic reduction and low latency. To reduce network traffic, we propose content-aware dynamic and seamless bitrate control that supports multi-channel real-time encoding for ABR, including 4K 60 fps video. Our method further supports chunked packaging transfer to provide low-latency streaming. We adopt a hybrid architecture consisting of hardware and software processing. The system consists of multiple 4K HEVC encoder LSIs that each LSI can encode 4K 60 fps or up to high-definition (HD) ×4 videos efficiently with the proposed bitrate control method. The software takes the packaging process according to the various streaming protocol. Experimental results indicate that our method reduces encoding bitrates obtained with constant bitrate encoding by as much as 56.7%, and the streaming latency over MPEG-DASH is 1.77 seconds.

  • QoE-Aware Stable Adaptive Video Streaming Using Proportional-Derivative Controller for MPEG-DASH Open Access

    Ryuta SAKAMOTO  Takahiro SHOBUDANI  Ryosuke HOTCHI  Ryogo KUBO  

     
    PAPER-Network

      Pubricized:
    2020/09/24
      Vol:
    E104-B No:3
      Page(s):
    286-294

    In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.

  • Methods for Adaptive Video Streaming and Picture Quality Assessment to Improve QoS/QoE Performances Open Access

    Kenji KANAI  Bo WEI  Zhengxue CHENG  Masaru TAKEUCHI  Jiro KATTO  

     
    INVITED PAPER

      Pubricized:
    2019/01/22
      Vol:
    E102-B No:7
      Page(s):
    1240-1247

    This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.