Performance Comparison of Transcoding and Bitrate-Aware Caching in Adaptive Video Streaming

Li, Wenjie; Oteafy, Sharief M. A.; Hassanein, Hossam S.

doi:10.1109/icc.2019.8761185

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…[27] introduced some policies for on-the-fly transcoding to improve transcoding efficiency. Li et al [28] compared the impact of transcoding and bitrate-aware caching on consumers' QoE through various experiments across different bandwidth patterns, cache capacities, and popularity skewness. The authors of [29] investigated the main issues for video transcoding and formulated resource provisioning for transcoding in the cloud and caching in the network to minimize resource consumption and QoE loss.…”

Section: B Transcoding Optimizationmentioning

confidence: 99%

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

Amirpour

Tashtarian

Timmerer

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge's capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose CD-LwTE, a Cost-and Delay-aware Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of CD-LwTE is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Videoon-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (i) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, CD-LwTE stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of CD-LwTE. We investigate the performance of CD-LwTE in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (i) reduces the transcoding time by up to 97%, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75%, and (iii) reduces delay by up to 48% compared to stateof-the-art approaches.

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Section: B Transcoding Optimizationmentioning

confidence: 99%

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

Amirpour

Tashtarian

Timmerer

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…They extended their work such as to minimize the expected video retrieval delay by determining the video request scheduling. Li et al [22] compared the impact of transcoding and bitrate-aware caching on consumers' Quality of Experience (QoE). Jin et al [21] proposed a partial transcoding approach at the edge servers.…”

Section: Related Workmentioning

confidence: 99%

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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MACC: MEC-Assisted Collaborative Caching for Adaptive Bitrate Videos in Dense Cell Networks

Guo

Yang

et al. 2022

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

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Performance Comparison of Transcoding and Bitrate-Aware Caching in Adaptive Video Streaming

Cited by 6 publications

References 17 publications

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

LwTE: Light-Weight Transcoding at the Edge

MACC: MEC-Assisted Collaborative Caching for Adaptive Bitrate Videos in Dense Cell Networks

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