On Optimizing Resource Utilization in AVC-based Real-time Video Streaming

Tashtarian, Farzad; Farahani, Reza; Timmerer, Christian; Hellwagner, Hermann

doi:10.1109/netsoft48620.2020.9165450

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…Note that this paper considered generating these streams in the network rather than at the source, otherwise this work could be considered a content-based technique. In different works, transcoding at the cloud edge is proposed to transform the requested video to specific formats [18]. Transcoding videos at channel change events requires a lot of processing complexity and even with complexity reductions around 82% compared to reference encoders [19], such processing is excessively expensive.…”

Section: Network-based Methodsmentioning

confidence: 99%

Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair

et al. 2021

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From a video coding perspective, there are two challenges when performing live video distribution over error-prone networks, such as wireless networks: random access and packet loss repair. There is a scarceness of solutions that do not impact steady-state usage and users with reliable connections. The proposed solution minimizes this impact by complementing a compression-efficient video stream with a companion stream solely consisting of keyframes. Although the core idea is not new, this paper is the first work to provide restrictions and modifications necessary to make this idea work using the High-Efficiency Video Coding (H.265/HEVC) compression standard. Additionally, through thorough quantification, insight is provided on how to provide low-latency fast channel switching capabilities and error recovery at low quality impact, i.e., less than 0.94 average Video Multimethod Assessment Fusion (VMAF) score decrease. Finally, worst-case drift artifacts are described and visualized such that the reader gets an overall picture of using the keyframe insertion technique.

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Section: Network-based Methodsmentioning

confidence: 99%

Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair

et al. 2021

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“…Solutions for L3 Streaming. Erfanian et al [17] proposed a framework for real-time streaming and transcoding at the edge of the network. They aggregate clients' requests at the edge servers, then transfer the highest requested bitrate from the origin server to the edge servers.…”

Section: Related Workmentioning

confidence: 99%

$\mathsf{HxL3}$: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming

Tashtarian

Bentaleb

Hellwagner

et al. 2023

IEEE Trans. Multimedia

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While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers' locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.

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“…In [4] and [5], we proposed OSCAR as a framework for real-time streaming. In OSCAR, we served clients' requests by minimizing bandwidth usage and transcoding costs.…”

Section: Related Workmentioning

confidence: 99%

“…If the required computational resource is more than the available computational resource (Φ) at the edge server, we set the lower boundary of x to the first element that its value in culT ran is greater than d (lines 3-4). For example, let assume culT ran= [1,2,4,5,7,9] (the most popular is the first element), Φ=6, and d = 9 − 6 = 3. In this case, the 'FindElement' function returns the third element with the value of 4 as the lower boundary of x .…”

Section: E Heuristic Algorithmmentioning

confidence: 99%

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LwTE: Light-Weight Transcoding at the Edge

et al. 2021

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Due to the growing demand for video streaming services, providers have to deal with increasing resource requirements for increasingly heterogeneous environments. To mitigate this problem, many works have been proposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in the cloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters, e.g., storage, computation, and bandwidth. In this paper, we propose LwTE, a novel Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). During the encoding process of a video segment at the origin side, computationally intense search processes are going on. The main idea of LwTE is to store the optimal results of these search processes as metadata for each video bitrate and reuse them at the edge servers to reduce the required time and computational resources for on-thefly transcoding. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction in bandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment is remarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided. Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD) streaming services by optimizing storage and computation (transcoding) costs at the edge servers and then compare it to conventional methods (store all bitrates, partial transcoding). The results indicate that our approach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to 70% compared to the state-of-the-art approaches.INDEX TERMS Video streaming, transcoding, video on demand, edge computing.

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On Optimizing Resource Utilization in AVC-based Real-time Video Streaming

Cited by 11 publications

References 29 publications

Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair

Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair

$\mathsf{HxL3}$: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming

LwTE: Light-Weight Transcoding at the Edge

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