Video Chunks Research Articles

Impacts of Device Caching of Content Fractions on Expected Content Quality

Motivated by the dynamic streaming feature that allows a video stream to consist of multiple chunks having different qualities, this study explores caching of fractions of a video content, not caching of an entire content, to increase the expected video quality. We show that the highest-quality content is better to be cached and propose the caching policy of video chunks having different qualities. Extensive performance evaluations are conducted to show that caching of content fractions, rather than an entire content, can improve the expected video quality especially when the channel condition is sufficiently good to cooperate with nearby BS or helpers.

Quality-Aware Deep Reinforcement Learning for Streaming in Infrastructure-Assisted Connected Vehicles

This paper proposes a deep reinforcement learning-based video streaming scheme for mobility-aware vehicular networks, e.g., vehicles on the highway. We consider infrastructure-assisted and mmWave-based scenarios in which the macro base station (MBS) cannot directly provide the streaming service to vehicles due to the short range of mmWave beams so that small mmWave base stations (mBSs) along the road deliver the desired videos to users. Motivated by the fact that a video stream consists of sequential chunks and smooth streaming has to be provided while users pass through multiple mBSs at high speed, the MBS proactively pushes some video chunks of the desired contents to mBSs. This is done to support vehicles that are currently covered and/or will be by each mBS. We formulate the dynamic video delivery scheme that adaptively determines 1) which content, 2) what quality and 3) how many chunks to be proactively delivered from the MBS to mBSs using Markov decision process (MDP). Since it is difficult for the MBS to track all the channel conditions and the network states have extensive dimensions, we adopt the deep deterministic policy gradient (DDPG) algorithm for the DRL-based video delivery scheme. This paper finally shows that the DRL agentlearns a streaming policy that pursues high average quality while limiting packet drops, avoiding playback stalls, reducing quality fluctuations and saving backhaul usage.

NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming

Video streaming services strive to support high-quality videos at higher resolutions and frame rates to improve the quality of experience (QoE). However, high-quality videos consume considerable amounts of energy on mobile devices. This paper proposes NeuSaver, which reduces the power consumption of mobile devices when streaming videos by applying an adaptive frame rate to each video chunk without compromising the user experience. NeuSaver generates a policy that can determine the appropriate frame rate for each video chunk using reinforcement learning (RL). The RL model automatically learns the policy that optimizes the QoE goals based on previous observations. NeuSaver also uses an asynchronous advantage actor-critic algorithm to reinforce the RL model quickly and robustly. Streaming servers that support NeuSaver preprocess videos into segments with various frame rates, which is similar to the process of creating videos with multiple bit rates in dynamic adaptive streaming over HTTP. NeuSaver utilizes the commonly used H.264 video codec. We evaluated NeuSaver in various experiments and a user study through four video categories along with the previously proposed model. Our experiments showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14% and up to 23.12% while maintaining high QoE.

Xatu: Richer Neural Network Based Prediction for Video Streaming

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.

VCMaker: Content-aware configuration adaptation for video streaming and analysis in live augmented reality

The emergence of edge computing has enabled mobile Augmented Reality (AR) on edge servers. We notice that the video configurations, i.e., frames per second (fps) and resolution, significantly affect the key metrics such as detection accuracy, data transmission latency and energy consumption in real AR application. Besides the time-varying bandwidth, we observe that the video contents, such as moving velocities of target objects, have remarkable impacts on the configuration selection. In addition, we take the energy consumption on data transmission into consideration. In this paper, we propose VCMaker, a system that generates video configuration decisions using reinforcement learning (RL). VCMaker trains a neural network model that selects configuration for future video chunks based on the collected observations. Rather than rely on any pre-programmed models, VCMaker learns to make configuration decisions solely through empirical observations of the resulting performances of historical decisions. In addition, we leverage the dynamic Region of Interest (RoI) encoding and motion vector-based object detection mechanisms to advance VCMaker. We implemented VCMaker and conducted extensive evaluations. The results show that VCMaker achieves a 20.5%–32.8% higher detection accuracy, and 25.2%–45.7% lower energy consumption than several state-of-the-art schemes.

The impact of lecture chunking format on university student vigilance: Implications for classroom pedagogy

Consistent with capacity theories of attention, attention can be sustained to the extent that spare mental resources remain available. The traditional lecture in higher education has received criticism for being too long to hold a student’s attention. This is based on several author’s claims that there is a measurable decrement in student attention after approximately 10-15 minutes of sustained content delivery. The present research aimed to investigate if providing small, separate units of an asynchronous lecture is able to enhance motivation for task engagement through perceived achievability of the learning outcomes, and consequently, enhance sustained attention amongst postgraduate university students. Utilising a quasi-experimental design, 51 postgraduate psychology students were recruited by opportunistic sampling from a cognitive psychology lecture on an MSc Psychology course, and given the option to watch either a long, single-video version of a lecture, or the same lecture delivered as smaller separate video chunks. Key findings indicate that presenting the material as smaller separate video units increased the perceived achievability of the learning outcomes and reduced the number of attention lapses experienced, but not the duration of those lapses, all measured via self-report single-item measures. The shorter separate videos condition also saw greater levels of break taking compliance. Looking at the sample as a whole using a hierarchical regression analysis, whilst controlling for student mind wandering tendencies as measured by the Mind Excessively Wandering Scale (MEWS), taking breaks was a significant negative predictor of attention lapses. Taken together, this suggests taking breaks is an integral part of sustained attention, and that chunking lectures into separate video units increases break taking compliance. Therefore, when designing online asynchronous learning material, lecturers should consider the value of chunking learning material for its potential direct and indirect effect on sustained attention.

Unveiling the End-User Viewport Resolution From Encrypted Video Traces

Video streaming is without doubt the most requested Internet service, and main source of pressure on the Internet infrastructure. At the same time, users are no longer satisfied by the Internet’s best effort service, instead, they expect a seamless service of high quality from the side of the network. As result, Internet Service Providers (ISP) engineer their traffic so as to improve their end-users’ experience and avoid economic losses. Content providers from their side, and to enforce customers privacy, have shifted towards end-to-end encryption (e.g., TLS/SSL). Video streaming relies on the dynamic adaptive streaming over HTTP protocol (DASH) which takes into consideration the underlying network conditions (e.g., delay, loss rate, and throughput) and the viewport capacity (e.g., screen resolution) to improve the experience of the end user in the limit of the available network resources. In this work, we propose an experimental framework able to infer fine-grained video flow information such as chunk sizes from encrypted YouTube video traces. We also present a novel technique to separate video and audio chunks from encrypted traces based on Gaussian Mixture Models (GMM). Then, we leverage our dataset to train models able to predict the class of viewport (either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SD</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HD</i> ) per video session with an average 92% accuracy and 85% F1-score. The prediction of the exact viewport resolution is also possible but shows a lower accuracy than the viewport class.

Video Super-Resolution and Caching—An Edge-Assisted Adaptive Video Streaming Solution

Edge computing provides the potential to improve users&#x2019; Quality of Experience (QoE) in ever-increasing video delivery. However, existing edge-based solutions cannot fully utilize the edge computing power and storage capacity. This paper proposes <b>VI</b>deo <b>S</b>uper-resolution and <b>CA</b>ching (VISCA), an edge-assisted adaptive video streaming solution, which integrates super-resolution and edge caching to improve users&#x2019; QoE. We design a novel edge-based ABR algorithm that makes bitrate and video chunk source decisions by considering network conditions, QoE objectives, and edge resource availability jointly. VISCA utilizes super-resolution to enhance the cached low-quality video at the edge. The super-resolution models used are trained for the most popular videos only in order to achieve quality improvements with a fraction of the computation. A novel cache strategy is also adopted to maximize caching efficiency. To assess the performance of VISCA, an implemented prototype of VISCA was deployed in synthetic and real network contexts. Compared with the existing video streaming solutions, VISCA improves video quality by 28.2&#x0025;-251.2&#x0025; and reduces rebuffering time by 16.1&#x0025;-95.6&#x0025; in all considered scenarios.

Exploiting layer and spatial correlations to enhance SVC and tile based 360-degree video streaming

Streaming 360-degree videos over today’s Internet is challenging due to its high bandwidth and low Motion-to-Photon latency requirements. Viewport adaptive streaming is a promising technique to cope with these challenges by allocating more bitrates to a user’s constrained field of view and streaming appropriate representations based on the user’s estimated throughput and fixation point. A typical dynamic adaptive streaming system uses a Media Presentation Description (MPD) file to describe the sustainable bitrates of a period for different quality representations. However, video bitrates vary significantly along the temporal dimension for Variable Bitrate (VBR) videos. In addition to the throughput overestimate/underestimate problem, video bitrate overestimate/underestimate also affects the efficiency of Adaptive Bitrate (ABR) algorithms.In this paper, we first discovered that for SVC and tile-based encoding, strong video chunk size correlation can be observed on both the layer and spatial dimensions for 360-degree videos. We then proposed to enhance the 360-degree video scheduling framework with a chunk size predictor that predicts the size of: (1) a future enhancement layer video chunk based on the corresponding base layer chunk of the same tiled segment; (2) future base layer video chunks based on the horizontally adjacent base layer video chunks. Experiments showed that ABR algorithms augmented by a chunk size predictor can significantly improve a user’s perceived video playback quality.

Cross-layer DASH-based multipath video streaming over LTE and 802.11ac networks

Dynamic Adaptive Streaming over Http (DASH) -based video streaming applications are becoming increasingly prevalent over the mobile Internet. Many efforts have been made to optimize their performances. Multipath video streaming that simultaneously utilizes multiple wireless networks for video content delivery is a common method. Another effective approach is the cross-layer video streaming optimization that jointly takes the parameters at different protocol layers into account. However, multipath streaming schemes mainly focus on how to efficiently utilize multiple wireless networks and the collaboration of parameters at different layers in each network is neglected. Likewise, the cross-layer schemes normally optimize the parameters at different layers in purely one network without fully utilizing the aggregated bandwidths of multiple available wireless networks. Therefore, both of them are sub-optimal and might suffer from degrading performance. In this paper, we propose a joint Cross-layer DASH-based multipath video streaming scheme that takes advantage of bandwidth aggregation of multiple wireless networks and further improves the performance by optimizing the different layers’ parameters in each network with a cross-layer manner. In the proposed scheme, the LTE and 802.11ac networks are adopted. The bitrate of DASH-based video chunk at application layer, the rate allocation among networks and the Modulation and Coding Scheme (MCS) at physical layers in LTE and 802.11ac downlink are jointly optimized. We also compare our proposed scheme to state-of-the-art schemes using trace-driven experiments. Experimental results show that our proposed scheme outperforms state-of-the-art schemes in terms of PSNR, normalized QoE, and balance between video bitrate and rebuffering penalty.

Deduplication-Oriented Mutual-Assisted Cooperative Video Upload for Mobile Crowd Sensing

Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency

Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices self-join subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

LensCast: Robust Wireless Video Transmission Over MmWave MIMO With Lens Antenna Array

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions. In LensCast, a transmission distortion minimization problem is formulated with the consideration of video chunk scheduling, path matching and power allocation, which is an intractable mixed integer non-linear programming (MINLP) problem. The solution of this MINLP problem is converted into resource allocation (i.e., joint path matching and power allocation) plus chunk scheduling. First, resource allocation is investigated with given chunk scheduling results. By analyzing the optimality of the resource allocation problem, a winner-takes-all assignment is obtained to guide resource allocation. After that, a greedy water-filling algorithm is proposed as a near-optimal solution. Second, we propose a low-complexity chunk scheduling algorithm to schedule chunks for each transmission. Simulation results demonstrate that the proposed LensCast achieves an improved performance in terms of both peak signal-to-noise ratio and visual quality comparing with reference schemes.

Quality-Aware Neural Adaptive Video Streaming With Lifelong Imitation Learning

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks' bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r.t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%-16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%-9.81% in comparison to the offline trained model.

Collaborative hierarchical caching and transcoding in edge network with CE-D2D communication

To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users’ devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost.

Heterogeneous Spatial Quality for Omnidirectional Video

Heterogeneous spatial quality in 360-degree videos is characterized by regions of high and low quality appearing in the same video chunk. Encoding quality-varying omnidirectional videos enables bandwidth waste optimization without compromising the quality inside the client's viewport. Heterogeneous spatial quality has been implemented using either the concept of tiling or Facebook's offset projection. In this paper, we study the preparation of heterogeneous 360-degree videos with three main contributions. We propose two novel approaches that prepare heterogeneous quality versions of a 360-degree video based on two video decomposition techniques: the Gaussian pyramid and the Laplace pyramid. We also introduce a spherical filter for omnidirectional videos which allows to further reduce the video bit-rate by exploring the spherical properties of the 360-degree videos. We compare the performance of the proposed three methods to tiling and the offset projection in representative scenarios of heterogeneous spatial quality in 360-degree videos and highlight the key trade-off between bit-rate and video quality to consider when implementing these approaches.

Requet

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for &lt;underline&gt; E &lt;/underline&gt;ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding

Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users’ quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The proposed metric prefers to use bandwidth of the links to optimize a concave utility function of the chunk quality. Even though the formulation is a non-convex discrete optimization, we provide a quadratic complexity algorithm which is shown to be optimal in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale. The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency.

PCCP: Proactive Video Chunks Caching and Processing in edge networks

Mobile Edge Computing (MEC) networks have been proposed to extend the cloud services and bring the cloud computing capabilities near the end-users at the Mobile Base Stations (MBS). To improve the efficiency of pushing the cloud features to the edge, different MEC servers assist each others to effectively select videos to cache and transcode. In this work, we adopt a joint caching and processing model for Video On Demand (VOD) in MEC networks. Our goal is to proactively cache only the chunks of videos to be watched and instead of caching the whole video content in one edge server (as performed in most of the previous works), neighboring MBSs will collaborate to store different video chunks to optimize the storage resources usage. Then, by coping with the Adaptive BitRate streaming technology (ABR), different representations of each chunk can be generated on the fly and cached in multiple MEC servers. To maximize the caching efficiency, we study the videos viewing pattern and design a Proactive caching Policy (PcP) and a Caching replacement Policy (CrP) to cache only highest probability video chunks. Servers performing caching and transcoding tasks should be thoroughly selected to optimize the storage and computing resources usage. Hence, we formulate this collaborative problem as a NP-hard Integer Linear Program (ILP). In addition to the CrP and PcP policies, we also propose a sub-optimal relaxation and an online heuristic, which are adequate for real-time chunks fetching. The simulation results prove that our model and policies perform more than 20% better than other edge caching approaches in terms of cost, average delay and cache hit ratio for different network configurations.