Content-Aware Personalised Rate Adaptation for Adaptive Streaming via Deep Video Analysis

Gao, Guanyu; Dong, Linsen; Zhang, Huaizheng; Wen, Yonggang; Zeng, Wenjun

doi:10.1109/icc.2019.8761156

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…3) Reinforcement Learning: Reinforcement learning has been applied in many areas for intelligent control, e.g., autonomous vehicle, video streaming [21], [22], resource provisioning [23], etc. It is concerned with how the agent should take actions in a dynamic environment to maximize the overall rewards [24].…”

Section: A Preliminary 1) Hvacmentioning

confidence: 99%

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

Gao

Wen

2020

IEEE Internet Things J.

Self Cite

185

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Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energyefficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants' thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants' thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants' thermal comfort by 13.6%.

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Section: A Preliminary 1) Hvacmentioning

confidence: 99%

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

Gao

Wen

2020

IEEE Internet Things J.

Self Cite

185

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“…Zhang et al [54] also developed a DeepQoE-based ABR system to verify that their framework can be easily applied to multimedia communication service. To address the challenge of how to allocate bitrate budgets for different parts of the video with different users' interest, Gao et al [55] proposed a content-of-interest-based rate adaptation scheme for ABR. They designed a deep learning approach for recognizing the interestingness of the video content and a DQN approach for rate adaptation according to incorporating video interestingness information.…”

Section: Drl-based Transcoding Schedulingmentioning

confidence: 99%

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Wan

2020

Wireless Communications and Mobile Computing

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In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

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“…Another alternative is to use computer vision models [27] to identify temporal key moments in a video. We show in Appendix D, however, that these models also fall short.…”

Section: Temporal Variability Of Quality Sensitivitymentioning

confidence: 99%

“…which can influence user experience and engagement (e.g., [14,22,36]). Various models have been developed to capture their effect [15,16,18,21,23,25,26] as well as users' attention over space (e.g., [42,43,59]) and time (e.g., [24,27,29]). SENSEI is complementary to these efforts: they apply the same heuristics to all videos, but SEN-SEI customizes itself for each video (in a cost-efficient way) to capture the true user sensitivity to video quality.…”

Section: Related Workmentioning

confidence: 99%

SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity

Zhang

Sen

et al. 2020

Preprint

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This paper aims to improve video streaming by leveraging a simple observation-users are more sensitive to low quality in certain parts of a video than in others. For instance, rebuffering during key moments of a sports video (e.g., before a goal is scored) is more annoying than rebuffering during normal gameplay. Such dynamic quality sensitivity, however, is rarely captured by current approaches, which predict QoE (quality-of-experience) using one-size-fits-all heuristics that are too simplistic to understand the nuances of video content, or that are biased towards the video content they are trained on (in the case of learned heuristics).The problem is that none of these approaches know the true dynamic quality sensitivity of a video they have never seen before. Therefore, instead of proposing yet another heuristic, we take a different approach: we run a separate crowdsourcing experiment for each video to derive users' quality sensitivity at different parts of the video. Of course, the cost of doing this at scale can be prohibitive, but we show that careful experiment design combined with a suite of pruning techniques can make the cost negligible compared to how much content providers invest in content generation and distribution. For example with a budget of just $31.4 per min video, we can predict QoE up to 37.1% more accurately than state-of-the-art QoE models.Our ability to accurately profile time-varying user sensitivity inspires a new approach to video streaming-dynamically aligning higher (lower) quality with higher (lower) sensitivity periods. We present a new video streaming system called SENSEI that incorporates dynamic quality sensitivity into existing quality adaptation algorithms. We apply SENSEI to two state-of-the-art adaptation algorithms, one rule-based and one based on deep reinforcement learning. SENSEI can take seemingly unusual actions: e.g., lowering bitrate (or initiating a rebuffering event) even when bandwidth is sufficient so that it can maintain a higher bitrate without rebuffering when quality sensitivity becomes higher in the near future. Compared to state-of-the-art approaches, SENSEI improves QoE by 15.1% or achieves the same QoE with 26.8% less bandwidth on average.

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Content-Aware Personalised Rate Adaptation for Adaptive Streaming via Deep Video Analysis

Cited by 17 publications

References 23 publications

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity

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