Dynamic Adaptive Streaming Over HTTP System Research Articles

COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth Utilization

Reaching close-to-optimal bandwidth utilization in dynamic adaptive streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the quality of experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm MinOff , which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding ( JITE ), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our JITE DASH system, which can offer a COBIRAS, in terms of bandwidth utilization and video QoE for different ABR algorithms.

Influence of video content type on the usefulness of reinforcement learning algorithms in DASH systems

The article presents the result of research on DASH (Dynamic Adaptive Streaming over HTTP) systems. In the proposed solution, the adaptive algorithm is based on the RL (Reinforcement Learning) paradigm. The Pensieve algorithm was chosen as the basis for the tests. This algorithm is widely discussed in the scientific literature and therefore the study and analysis of its properties is useful in a wide range of solutions using DASH. The main contribution of the presented test results to the development of knowledge on video streaming services consists in the analysis of the impact of the characteristics of video materials on the effectiveness of the adaptation process implemented by the developed RL model. The presented results show that this influence should not be omitted in any in-depth analyses of the characteristics of DASH systems.

A Framework for Multi-Task Learning in Dynamic Adaptive Streaming Over HTTP

This paper presents a framework with a taxonomy for multi-task learning in the context of Dynamic Adaptive Streaming over HTTP (DASH). DASH is a widely used technology for video streaming, and multi-task learning has emerged as a promising approach to enhance the performance and user experience of DASH systems by jointly optimizing multiple related tasks. The framework provides a structured approach to design, train, and evaluate multi-task learning models in DASH, while the taxonomy categorizes the key components and approaches within the framework. The taxonomy includes task types, multitask learning approaches, input features, and training strategies. Task types encompass video quality adaptation, buffer management, bandwidth estimation, content pre-fetching, and resource allocation, representing the specific tasks involved in DASH. Multi-task learning approaches encompass methodologies such as shared representation learning, task-specific layers, multi-head architectures, knowledge distillation, and reinforcement learning, offering flexibility in model design and optimization. Input features cover video characteristics, network conditions, device capabilities, and user preferences, providing the necessary information for informed decision-making across tasks. Training strategies include joint training, alternate training, hierarchical training, task weighting, and task balancing, determining how the multi-task learning model is trained and optimized. By following the presented framework and taxonomy, researchers and practitioners can systematically approach the design, training, and evaluation of multi-task learning models in DASH. The framework enables the development of efficient and adaptive video streaming systems by leveraging the interdependencies among tasks. The taxonomy helps organize the components and approaches within the framework, aiding in a better understanding of the various aspects of multi-task learning in the DASH context. Overall, this framework and taxonomy provide a valuable resource for advancing the field of multitask learning in the dynamic and complex domain of video streaming over HTTP.

A Fast FoV-Switching DASH System Based on Tiling Mechanism for Practical Omnidirectional Video Services

With the development of multimedia technologies and virtual reality display devices, omnidirectional videos have gained popularity nowadays. To reduce the bandwidth requirement for omnidirectional video transmission, tile-based viewport adaptive streaming methods have been proposed in the literatures. Challenges related to decoding the tiles simultaneously with limited number of decoders, and ensuring user's viewing experience during the viewport switch are still to be solved. In this paper, a two-layer fast viewport switching dynamic adaptive streaming over HTTP (DASH) system based on tiling mechanism is proposed, which incorporates the viewing trajectory of end users. To deal with the simultaneously decoding problem, an open group of picture (GOP) technique is proposed to enable merging different types of tiles into a composite stream at the client side. To reduce the quality recovery duration after the viewport change, a fast-switching strategy is also proposed. Moreover, considering the priorities of different types of chunks, a download strategy is further proposed to adapt the bandwidth fluctuations and viewport changes. Experimental results showed that the proposed system can significantly reduce the recovery duration of high quality video by approximately 90%, which can provide a better viewing experience to end users.

Automatic QoE evaluation for asymmetric encoding of 3D videos for DASH streaming service

The paper is based on the study of the performance of a Dynamic Adaptive Streaming over HTTP (DASH) system in the context of 3D video streaming, using different scenarios and network conditions, specifically with bandwidth variations. The objective is the development of a framework for the evaluation of QoE in 3D adaptive video streaming scenarios, which allows to analyze the impact on the user's Quality of Experience (QoE) using different bandwidth variation patterns (switching frequency, range and type of variation), among other aspects. A set of subjective tests will be carried out, with the aim of identifying the correlation between the quality of the user experience and the frequency, type, range and temporal location of the bandwidth switching events. The proposed framework allows performance measurements to be carried out in an automated and systematic way for the evaluation of DASH systems in 2D and 3D video streaming service. We have used Puppeteer, the Node.js library developed by Google, which provides a high-level API, to automate actions on Chrome Devtools Protocol, such as starting playback, causing bandwidth changes and saving the results of quality change processes, timestamps, stalls and so on. From this data, a processing is made to allow the reconstruction of the visualized video, as well as the extraction of quality metrics and the users’ QoE assessment using the ITU-T P.1203 recommendation.Categories and Subject DescriptorsC.2.1 [Computer Systems Organization]: Computer Communications Networks– Network Architecture and Design, wireless communication

Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

Dynamic Adaptive Streaming based on Deep Reinforcement Learning

Dynamic adaptive streaming over HTTP (DASH) is the dominant technology of multimedia delivery over the Internet. In DASH system, adaptive bitrate (ABR) algorithms running on client-side video player are the key to improve user quality of experience (QoE). However, most existing ABR algorithms employ fixed control rules to make bitrate decisions based on throughput, playback buffer size, or a combination of the two. As a result, their performance in the complicated and fluctuant network environment is incompetent. In this paper, we propose QRL, a bitrate adaptation approach based on deep reinforcement learning. QRL uses double Q-Learning, an enhanced Q-Learning method. After training the neural network model, the algorithm can select proper bitrates for future video segments based on all the information collected by client during the video playback process. Simulation results show that QRL achieves better performance than other algorithms.

Cooperative Bargaining Game-Based Multiuser Bandwidth Allocation for Dynamic Adaptive Streaming Over HTTP

Dynamic adaptive streaming over HTTP (DASH) has emerged as an efficient technology for video streaming. For a DASH system, a most common case is that a limited server bandwidth is competed by multiusers. In order to improve user quality of experience (QoE) and guarantee fairness, we propose to use the game theory in a proxy server to allocate the bandwidth collaboratively for multiusers. By taking user buffer length, received video bit rates, video qualities, etc., into account, the bandwidth allocation problem is formulated as a cooperative bargaining problem and the Nash bargaining solution (NBS) is obtained by convex optimization. The requested bit rate of users will be rewritten as the proxy calculated bit rate (i.e., NBS) when the user requested bit rate is larger. Experimental results demonstrate that user QoE and fairness can be improved significantly, i.e., the delay frequency and duration are smaller, and the received video qualities are higher and more stable, when comparing the proposed method with existing methods.

A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming Over HTTP

With companies such as Netflix and YouTube accounting for more than 50% of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field.

Adaptive Streaming in P2P Live Video Systems

Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows us to leverage the cooperation among peers, guaranteeing the service of video requests with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to provide its users with very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, in terms of both robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.