Dynamic Adaptive Streaming Over HTTP Video Research Articles

Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTP

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.

A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP

Deep Learning (DL) has become a fundamental technology in the field of Dynamic Adaptive Video Streaming over HTTP (DASH), enabling significant advancements in video streaming systems. This taxonomy presents a novel framework for categorizing and organizing the diverse applications and methodologies of DL in DASH. The taxonomy encompasses various aspects of DL, including video representation, quality of experience (QoE) estimation, bitrate adaptation, buffer management, content- and context-aware adaptation, and network optimization. By providing a comprehensive overview of DL in DASH, this taxonomy serves as a valuable resource for researchers and practitioners, facilitating a better understanding of the different DL techniques and their applications in enhancing video streaming performance and user experience.

Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications

Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.

Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

CoLEAP: Cooperative Learning-Based Edge Scheme With Caching and Prefetching for DASH Video Delivery

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

New objective QoE models for evaluating ABR algorithms in DASH

As users become more demanding with regards to the consumption of multimedia content, the importance of measuring their level of satisfaction is growing. The difficulty in terms of time and resources for assessing the Quality of Experience (QoE) has popularized the use of objective QoE models, which try to emulate human behavior regarding the playback of multimedia streaming. Some objective QoE models existing in the literature are based on the bitrate. However, the PSNR (Peak Signal-to-Noise Ratio) or VMAF (Video Multimethod Assessment Fusion) have been proved to be metrics with a closer relationship with the QoE than the bitrate. This paper proposes three new models to measure the QoE analytically in DASH (Dynamic Adaptive Streaming over HTTP) video services. The first is based on the bitrate of the displayed video segments, whereas the second and the third are based on the PSNR and VMAF of each video segment, respectively. The proposed models are compared to the ITU-T standard P.1203 as well as the bitrate-based QoE model proposed by Yin et al. Moreover, the paper presents a subjective study, which confirms the validity of the proposed models. The models are validated by using different DASH adaptation algorithms. In this sense, this paper also presents a DASH ABR (Adaptive Bitrate Streaming) algorithm called Look Ahead, which takes into account the inherent bitrate variability of the video encoding process in order to calculate, in real time, the appropriate quality level that minimizes the number of stalls during the playback.

Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP

The article presents a novel affective content-aware adaptation scheme (ACAA) to optimize Quality of Experience (QoE) for dynamic adaptive video streaming over HTTP (DASH). Most of the existing DASH adaptation schemes conduct video bit-rate adaptation based on an estimation of available network resources, which ignore user preference on affective content (AC) embedded in video data streaming over the network. Since the personal demands to AC is very different among all viewers, to satisfy individual affective demand is critical to improve the QoE in commercial video services. However, the results of video affective analysis cannot be applied into a current adaptive streaming scheme directly. Correlating the AC distributions in user's viewing history to each being streamed segment, the affective relevancy can be inferred as an affective metric for the AC related segment. Further, we have proposed an ACAA scheme to optimize QoE for user desired affective content while taking into account both network status and affective relevancy. We have implemented the ACAA scheme over a realistic trace-based evaluation and compared its performance in terms of network performance, QoE with that of Probe and Adaptation (PANDA), buffer-based adaptation (BBA), and Model Predictive Control (MPC). Experimental results show that ACAA can preserve available buffer time for future being delivered affective content preferred by viewer's individual preference to achieve better QoE in affective contents than those normal contents while remain the overall QoE to be satisfactory.

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.

QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.

Evaluation of Throughput Prediction for Adaptive Bitrate Control Using Trace-Based Emulation

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

QoE-Traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming

Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.

Compete or Collaborate: Architectures for Collaborative DASH Video Over Future Networks

Dynamic adaptive streaming over HTTP (DASH) clients compete with each other over one or more bottleneck links in a network, which results in fluctuations in TCP throughput and QoE, QoE unfairness among clients, and underutilization of the network capacity. We propose centralized and distributed architectures for collaboration between network service provider (NSP), video service provider (VSP), and users (DASH clients) to provide NSP-managed or VSP-managed DASH services over software-defined networks (SDN) with quality-of-service (QoS) reserved network slices. We show that QoS reservation alone is not sufficient to overcome QoE fluctuations per client and unfairness between heterogeneous video clients, and clients also need to employ TCP receive-window adaptation knowing their fair-share bitrate. To this effect, we propose two collaborative streaming service models to inform clients about their fair-share bitrates. We first present an NSP-managed service model with centralized collaboration between the NSP, VSP, and the users, where a traffic engineering manager at the NSP assigns a fair-share bitrate to each DASH client. We then present a VSP-managed service model with centralized or distributed collaboration architectures, where in the former the VSP determines the fair-share bitrate for each client over a reserved network slice and in the latter a group of DASH clients sharing a reserved network slice collaborate among themselves. In the novel distributed collaboration framework, collaboration groups are identified by the VSP, and clients within a group share critical parameters with each other so that each client can estimate its fair-share bitrate. Experimental results demonstrate that collaboration rather than competition between clients not only helps them achieve a smooth goodput near their fair-share bitrate, but also improves the total goodput over the reserved slice.

Cache'n DASH

HTTP-based video streaming services have been dominating the global IP traffic over the last few years. Caching of video content reduces the load on the content servers. In the case of Dynamic Adaptive Streaming over HTTP (DASH), for every video the server needs to host multiple representations of the same video file. These individual representations are further broken down into smaller segments. Hence, for each video the server needs to host thousands of segments out of which, the client downloads a subset of the segments. Also, depending on the network conditions, the adaptation scheme used at the client-end might request a different set of video segments (varying in bitrate) for the same video. The caching of DASH videos presents unique challenges. In order to optimize the cache hits and minimize the misses for DASH video streaming services we propose an Adaptation Aware Cache (AAC) framework to determine the segments that are to be prefetched and retained in the cache. In the current scheme, we use bandwidth estimates at the cache server and the knowledge of the rate adaptation scheme used by the client to estimate the next segment requests, thus improving the prefetching at the cache.

Detecting Period and Burst Durations in Video Streaming by Means of Active Probing

The growth of broadband Internet access across the world for the last 10 years has made new business models possible. Services which before mainly were provided by network operators and in dedicated networks have now migrated to the open Internet. This has created a global service provider market, using the Internet as platform. The network operators are in many cases left with only providing the broadband access service. For the global service providers, continuous effort is put into the field of finding smart methods for bringing new and advanced services to the market without asking for e.g. QoS features from the involved network operators. This type of service delivery is called Over-The-Top (OTT). The concept of making services able to adapt their network and transport requirements during time of delivery is a strong contribution to success for OTT services.The focus of the work in this thesis, is methods for improving various aspects - as defined by my research questions - related to QoS and potentially also QoE for dynamic adaptive video streaming over HTTP (DASH) services. The motivation for focusing on video services is based on their high QoS requirements (i.e. bandwidth) and also popularity in terms of usage.In the work presented in this thesis, I have studied the behaviour and performance of DASH services by means of simulations, measurements and experiments. Based on insight obtained through this, I established a hypothesis on how QoS and QoE aspects could be improved for users present in the same home network environment. My hypothesis was that making more accurate information available about both services and network conditions in near real-time could facilitate improved control methods. The effectiveness of my suggested methods have in most cases been analysed by means of implementation in an experimental lab scenario, and supported by simulations and analytical approaches when appropriate.The findings presented in the included papers are all closely related and map into the research model used. This model is composed of Knowledge Plane, Monitor Plane and Action plane components located in both service endpoints and involved network components. As my main focus has been on OTT service delivery, the main contributions of my work apply to service endpoints, i.e. components in the home network and on the server side. The server side would in many cases be represented by a Content Delivery Network (CDN) node.The main research questions identified are:RQ1: In a home network how to (autonomously) control the performance of DASH based services.RQ2: How to provide fairness and stability for competing DASH sessions using service endpoint functionality.RQ3: How to choose appropriate quality levels for a DASH session.The main contributions from my research are:C1: A method for controlling the quality levels of DASH in the home gateway.C2: A method for improving fairness among competing DASH sessions.C3: A method for shaping traffic aggregates on access links with DASH components.C4: A method for estimating available bandwidth on access links when DASH sessions are present.

Estimating Available Bandwidth on Access Links by Means of Stratified Probing

The growth of broadband Internet access across the world for the last 10 years has made new business models possible. Services which before mainly were provided by network operators and in dedicated networks have now migrated to the open Internet. This has created a global service provider market, using the Internet as platform. The network operators are in many cases left with only providing the broadband access service. For the global service providers, continuous effort is put into the field of finding smart methods for bringing new and advanced services to the market without asking for e.g. QoS features from the involved network operators. This type of service delivery is called Over-The-Top (OTT). The concept of making services able to adapt their network and transport requirements during time of delivery is a strong contribution to success for OTT services.The focus of the work in this thesis, is methods for improving various aspects - as defined by my research questions - related to QoS and potentially also QoE for dynamic adaptive video streaming over HTTP (DASH) services. The motivation for focusing on video services is based on their high QoS requirements (i.e. bandwidth) and also popularity in terms of usage.In the work presented in this thesis, I have studied the behaviour and performance of DASH services by means of simulations, measurements and experiments. Based on insight obtained through this, I established a hypothesis on how QoS and QoE aspects could be improved for users present in the same home network environment. My hypothesis was that making more accurate information available about both services and network conditions in near real-time could facilitate improved control methods. The effectiveness of my suggested methods have in most cases been analysed by means of implementation in an experimental lab scenario, and supported by simulations and analytical approaches when appropriate.The findings presented in the included papers are all closely related and map into the research model used. This model is composed of Knowledge Plane, Monitor Plane and Action plane components located in both service endpoints and involved network components. As my main focus has been on OTT service delivery, the main contributions of my work apply to service endpoints, i.e. components in the home network and on the server side. The server side would in many cases be represented by a Content Delivery Network (CDN) node.The main research questions identified are:RQ1: In a home network how to (autonomously) control the performance of DASH based services.RQ2: How to provide fairness and stability for competing DASH sessions using service endpoint functionality.RQ3: How to choose appropriate quality levels for a DASH session.The main contributions from my research are:C1: A method for controlling the quality levels of DASH in the home gateway.C2: A method for improving fairness among competing DASH sessions.C3: A method for shaping traffic aggregates on access links with DASH components.C4: A method for estimating available bandwidth on access links when DASH sessions are present.