With internet video gaining increasing popularity and soaring to dominate network traffic, extensive studies are being carried out on how to achieve higher Quality of Experience (QoE) with the delivery of video conten...
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With internet video gaining increasing popularity and soaring to dominate network traffic, extensive studies are being carried out on how to achieve higher Quality of Experience (QoE) with the delivery of video content. Associated with the chunk-based streaming protocol, adaptivebitrate (ABR) algorithms have recently emerged to copewith the diverse and fluctuating network conditions by dynamically adjusting bitrates for future chunks. This inevitably involves predicting the future throughput of a video session. Some of the session features like Internet Service Provider (ISP), geographical location, and so on, could affect network conditions and contain helpful information for this throughput prediction. In this article, we consider how our knowledge about the session features can be utilized to improve ABR quality via customized parameter settings. We present our ABR-independent, QoE-driven, feature-based partition method to classify the logged video sessions so that different parameter settings could be adopted in different situations to reach better quality. A variation of Decision Tree is developed for the classification and has been applied to a sample ABR for evaluation. The experiment shows that our approach can improve the average bitrate of the sample ABR by 36.1% without causing the increase of the rebuffering ratio where 99% of the sessions can get improvement. It can also improve the rebuffering ratio by 87.7% without causing the decrease of the average bitrate, where, among those sessions involved in rebuffering, 82% receives improvement and 18% remains the same.
Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditi...
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ISBN:
(纸本)9781450355674
Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.
Video players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. ...
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ISBN:
(纸本)9781450376426
Video players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. To this end, we propose a novel deep reinforcement learning (DRL) based live video streaming optimization approach. Firstly, we point out the optimization objectives by comparing the difference between the VoD scenario and the live video streaming scenario. Then, according to the optimization conditions, we establish QoE optimization model in combination with a state-of-the-art DRL algorithm. We compare our algorithm with state-of-the-art ABR algorithms in a simulator with real-world video and network trace. Simulation results show that the proposed algorithm improves user experience quality by 5.6% on average, compared with existing algorithms.
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