Optimizing Quality of Experience for Adaptive Bitrate Streaming via Viewer Interest Inference

Gao, Guanyu; Zhang, Huaizheng; Hu, Han; Wen, Yonggang; Cai, Jianfei; Luo, Chong; Zeng, Wenjun

doi:10.1109/tmm.2018.2838330

Cited by 36 publications

(10 citation statements)

References 40 publications

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“…In the reinforcement learning setting, we consider the user experience 𝑄𝑜𝐸 𝑡 𝑢,𝑣 as the reward that viewer 𝑢 provides to the tracker. We first divide the live video stream in multiple segments 𝑐 [16,31], and then calculate the user experience 𝑄𝑜𝐸 𝑡 𝑢,𝑣 of the connection 𝑒 𝑡 𝑢,𝑣 at the 𝑡-th minute by considering the following factors [25,45]:…”

Section: Proposed Model 21 Quality Of User Experience In Live Video S...mentioning

confidence: 99%

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

Antaris¹,

Rafailidis²,

Girdzijauskas³

2021

Preprint

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In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to lowbandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net

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Section: Proposed Model 21 Quality Of User Experience In Live Video S...mentioning

confidence: 99%

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

Antaris¹,

Rafailidis²,

Girdzijauskas³

2021

Preprint

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“…And we suppose that each version of the same video has the same request probability. Also set τ n0 = 100ms, τ nn 0 is evenly distributed in [5,50] ms interval, the number of edge servers is 10. The unit bit transcoding time β n reference value is uniformly set to 2 μs, and the edge server cache capacity α reference value is set to 50.…”

Section: Pyramid Group Intelligent Evolutionary Solution Algorithm Bamentioning

confidence: 99%

“…Due to differences in user own hardware processing capabilities, network channel conditions, etc., different users usually request video files of different quality from remote video storage devices [3]. According to behavioral characteristics of users, Adaptive Bit Rate (ABR) technology is widely used in video services to improve QoE of users [4,5].…”

Section: Introductionmentioning

confidence: 99%

Joint optimization strategy for QoE-aware encrypted video caching and content distributing in multi-edge collaborative computing environment

2020

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The video request service of users in 5G network will explode, and adaptive bit rate technology can provide users with reliable video response. Placing video resources on edge servers close to users can overcome the problem of excessive network load similar to traditional centralized cloud platform solutions. Moreover, multiple edge servers can provide caching and transcoding support by collaboration mechanisms, which further improves users’ Quality of Experience (QoE). However, the design difficulty of video caching and content distribution strategies is increased due to the diversity of collaboration mechanisms and the competition between local and collaborative services of edge servers for computing and storage resources. In order to solve this problem, video cache and content distribution problem is modeled as random integer programming problem in the multi-edge server at most two-hop collaboration scenario. In order to improve the security of video data transmission, the video stream is encrypted using an encryption algorithm based on Logistic chaotic-Quantum-dot Cellular Automata (QCA). For improving the efficiency of solving integer programming problems, this paper uses a pyramid intelligent evolution algorithm based on optimal cooperation strategy to solve this problem. Simulation experiments show that our proposed method can obtain higher QoE value compared with several newer methods. In addition, the average access delay of proposed method is shortened by more than 27.98%, which verifies its reliability.

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“…For example, Ma et al [3] consider these tradeoffs in the context of interactive multiview streaming. HAS also has been leveraged for bandwidth-aware support of other interactive services, including interactive multiview streaming [4], optimized stream bundles [6], and to enhance parts of regular (linear) videos that the users show more interest in [34].…”

Section: Related Workmentioning

confidence: 99%

Cross-user Similarities in Viewing Behavior for 360$^{\circ}$ Video and Caching Implications

Carlsson,

Eager

2019

Preprint

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The demand and usage of 360 • video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edgenetwork on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360 • view into multiple tiles, and adapt the quality of the downloaded tiles based on the user's expected viewing direction and bandwidth conditions. To better understand the potential bandwidth savings that caching-based techniques may offer for this context, this paper presents the first characterization of the similarities in the viewing directions of users watching the same 360 • video, the overlap in viewports of these users (the area of the full 360 • view they actually see), and the potential cache hit rates for different video categories, network conditions, and accuracy levels in the prediction of future viewing direction when prefetching. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and can inform the design of new caching system policies tailored for 360 • video.

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Optimizing Quality of Experience for Adaptive Bitrate Streaming via Viewer Interest Inference

Cited by 36 publications

References 40 publications

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

Joint optimization strategy for QoE-aware encrypted video caching and content distributing in multi-edge collaborative computing environment

Cross-user Similarities in Viewing Behavior for 360$^{\circ}$ Video and Caching Implications

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