Scalable Video Streaming Research Articles

Adaptive Scalable Video Streaming (ASViS): An Advanced ABR Transmission Protocol for Optimal Video Quality

Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces the Adaptive Scalable Video Streaming (ASViS) protocol as a solution. ASViS incorporates scalable video coding, a flow-controlled User Datagram Protocol (UDP), and deadline-based criteria. A model is developed to predict the behavior of ASViS across varying network conditions. Additionally, the effects of diverse parameters on ASViS performance are evaluated. ASViS adjusts data flow similarly to the Transmission Control Protocol (TCP), based on bandwidth availability. Data are designed to be discarded by ASViS according to video frame deadlines, preventing outdated information transmission. Compliance with RFC 8085 ensures the internet is not overwhelmed. With its scalability feature, ASViS achieves the highest possible image quality per frame, aligning with Scalable Video Coding (SVC) and the available data layers. The introduction of ASViS offers a promising approach to address the challenges faced by DASH, potentially providing more consistent and higher-quality video streaming.

Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HAS

HTTP Adaptive Streaming (HAS) plays a key role in over-the-top video streaming with the ability to reduce the video stall duration by adapting the quality of transmitted video segments to the network conditions. However, HAS still suffers from two problems. First, it incurs variations in video quality because of throughput fluctuation. Adaptive bitrate (ABR) algorithms at the HAS client usually select a low-quality segment when the throughput drops to avoid stall events, which impairs the Quality of Experience (QoE) of the end-users. Second, many ABR algorithms choose the lowest-quality segments at the beginning of a video streaming session to ramp up the playout buffer early on. Although this strategy decreases the startup time, clients can be annoyed as they have to watch a low-quality video initially. To address these issues, we introduced the H2BR technique (H TTP/2-B ased R etransmission) (Nguyen et al. 33) that utilizes certain features of HTTP/2 (including server push, multiplexing, stream priority, and stream termination) for late transmissions of higher-quality versions of video segments already in the client buffer, in order to improve video quality. Although H2BR was shown to enhance the QoE, limited streaming scenarios were considered resulting in a lack of general conclusions on H2BR’s performance. Thus, this article provides a profound evaluation to answer three open questions: (i) how H2BR’s performance is impacted by parameters at the server side (i.e., various encoding specifications), at the network side (i.e., packet loss rate), and at the client side (i.e., buffer size) on the performance of H2BR; (ii) how H2BR outperforms other state-of-the-art approaches in different configurations of the parameters above; (iii) how to effectively utilize H2BR on top of ABR algorithms in various streaming scenarios. The experimental results show that H2BR’s performance increases with the buffer size and decreases with increasing packet loss rates and/or video segment duration. The number of quality levels can negatively or positively impact on H2BR’s performance, depending on the ABR algorithm deployed. In general, H2BR is able to enhance the video quality by up to 17% and 14% in scalable video streaming and in non-scalable video streaming, respectively. Compared with an existing retransmission technique (i.e., SQUAD Wang et al., ACM Trans Multimed Comput Commun Applic (TOMM) 13(3s): 45, 49), H2BR shows better results with more than 10% in QoE and 9% in the average video quality.

Multi-Connection Based Scalable Video Streaming in UDNs: A Multi-Agent Multi-Armed Bandit Approach

Scalable video coding (SVC) has received much attention for video transmission over wireless due to its flexibility. However, most previous work only considered SVC video streaming from a single base station (BS). At present, the densification of BSs enables a user equipment (UE) to connect to multiple BSs in ultra-dense networks (UDNs). In this paper, we consider the problem of SVC video streaming in a UDN, which allows different layers of a video block to be downloaded from different BSs. An optimization problem is formulated aiming to maximize the quality of experience (QoE) of users by selecting the optimal connection strategy and optimal number of video layers. Considering the complexity, to efficiently solve the problem in a distributed manner, the problem of choosing connection strategy is formulated as a multi-agent multi-armed bandit (MA-MAB) problem with only few information exchange. Each user can adapt its connection strategy in a distributed self-learning system. To obtain the optimal arm for the MA-MAB problem, we propose a multi-user arm decision algorithm. To avoid large computation and handover costs, we adopt the same connection strategy for the entire video sequence. Then for each video block, with the given connection strategy, the number of video layers is adjusted adaptively according to dynamic network conditions. Finally, based on the above designs, we provide the SVC-based video downloading scheme to obtain an approximate optimal solution to the original optimization problem. Extensive simulations and comparisons show the feasibility and superiority of the proposed scheme.

Joint multipath flow and layer allocation for scalable video streaming

Projections estimating future online traffic reveal that better techniques are required to meet the expected demand. An emerging network paradigm, software defined networking (SDN) provides effective central management in routing and resource management. In this article, we define and solve a jointly optimal bandwidth allocation and multipath routing problem for SDN-based delay/jitter constrained transmission of scalable videos. We also develop a heuristic approach that can easily be implemented in an SDN controller. Two different configurations of the problem are simulated. Numerical evaluations show that proposed multipath routing solutions provide considerable improvement in both configurations when compared to benchmark algorithms. Mininet emulation results also agree with the theoretical results.

Cooperation Between LDM-Based Terrestrial Broadcast and Broadband Unicast: On Scalable Video Streaming Applications

This article investigates the reliability gain of broadcast (BC)/broadband (BB) cooperation for mobile scalable video streaming. A specific form of BC-BB cooperation is introduced, which is referred to as cooperative casting. The proposed system primarily utilizes a BC transmission for streaming, while BB backup transmissions are requested whenever the BC signal is not decodable. For BC deliveries in this system, a cross-layer collaboration between scalable video coding (SVC) and layered-division multiplexing is used to improve spectral efficiency by coupling their hierarchical structures. In this end, in tandem with designing the uplink requesting rules based on channel status, delivery performance of the cooperative casting is analytically evaluated in a probabilistic framework. The system design and analyses presented in this article focus on the service continuity in a mobile and time-varying channel, where the physical aspects, such as delayed unicast response and inter-carrier interference, are carefully taken into account. Compared to the standalone network transmissions, the cooperative casting is numerically verified to better prevent the delivery failures in high-speed mobile channels, especially when the SVC layer-combined (full-scale video) service is intended. Furthermore, it is also shown that relaxing the criteria for the BB stream requesting can reduce delivery failures substantially, while it increases transmission redundancy.

QoE-Aware Multi-Source Video Streaming in Content Centric Networks

Content Centric Networking (CCN), a future Internet architecture, brings new challenges in maintaining the Quality of Experience (QoE) for video streaming. Because of the universal caching capability of CCN routers, streaming from multiple sources will be common, and switching between content sources might affect QoE by inducing delays and consequently stalls in video playback. This paper proposes a new QoE-aware multi-source video streaming scheme for CCN. First, the content distributions of video files among CCN nodes for different caching methods are studied. Second, an adaptive video streaming with distributed caching (ASDC) algorithm is designed to guarantee QoE during the switching between content sources. The ASDC algorithm considers the delivery of scalable video streams. It automatically adapts the layers in a video stream when there is source switching, based on a QoE model that characterizes the effect of stalling. Experimental results show that the ASDC algorithm outperforms dynamic adaptive streaming over HTTP (DASH) in the CCN platform in terms of the QoE obtained from human subjective tests.

Dynamic Resource Allocation for Scalable Video Streaming in OFDMA Wireless Networks

Mobile video streaming is a successful example of Cyber-Physical-Social Systems (CPSS). How to schedule network resources and provide better mobile video streaming services for mobile users are very important. Scalable video streaming is regarded as a promising technology in wireless networks where the cognitive femtocells are overlaid within the coverage area of a macrocell network. In this paper, we study dynamic resource allocation for scalable video streaming over cache-enabled wireless networks with time-varying channel conditions. We formulate the scalable video streaming problem as a stochastic optimization problem which aims at maximizing the time-averaged system utility subject to the time-averaged video cache constraint at the server and the cross-tier interference constraint on the primary user under the sparse deployment scenario of femtocells. By employing the Lyapunov optimization theory, we design a dynamic cache and resource allocation (DCRA) algorithm to solve this problem. Furthermore, the problem is decomposed into three subproblems, i.e., video layer selection, cache placement, and wireless resource allocation. Via solving these subproblems, we derive the video layer selection and cache placement strategies, and a wireless resource allocation algorithm to manage the cross-tier interference. Simulation results demonstrate the advantages of the proposed DCRA for streaming scalable video over time-varying wireless networks.

Quality Adaptation and Resource Allocation for Scalable Video in D2D Communication Networks

Recently fifth-generation (5G) of cellular networks appeared to enable various smart bandwidth thirsty applications. Frequency re-usability via device-to-device (D2D) communication is one of the possible ways to enhance the 5G network throughput. However, despite these technological advancements in cellular networks, users’ Quality of Experience (QoE) is significantly affected by video quality adaptation according to future network conditions for video streaming. In this paper, we jointly formulate D2D pair association, video quality adaptation, and network resource allocation for scalable video streaming in a D2D communication network. The formulated problem is a mixed-integer linear programming problem and has a combinatorial nature. Thus, the formulated problem is decomposed into two subproblems: D2D pair association subproblem and resource allocation subproblem. The D2D pair association subproblem is a combinatorial optimization problem, to solve it a distributed algorithm is proposed based on two sided-matching theory. On the other hand, resource allocation subproblem is solved using conventional linear optimization techniques to get the boundary solution. Furthermore, a fully distributed quality adaptation scheme is presented using the proposed resource allocation algorithm for layered video. To validate the performance of our proposal, we perform numerical simulations for various scenarios.

Dynamic Resource Allocation for Streaming Scalable Videos in SDN-Aided Dense Small-Cell Networks

Both wireless small-cell communications and software-defined networking (SDN) in wired systems continue to evolve rapidly, aiming for improving the quality of experience (QoE) of users. Against this emerging landscape, we conceive scalable video streaming over SDN-aided dense smell-cell networks by jointly optimizing the video layer selection, the wireless resource allocation, and the dynamic routing of video streams. In the light of this ambitious objective, we conceive a dense software-defined small-cell network architecture for the fine-grained manipulation of the video streams relying on the cooperation of small-cell base stations. Based on this framework, we formulate the scalable video streaming problem as maximizing the time-averaged QoE subject to a specific time-averaged rate constraint as well as to a resource constraint. By employing the classic Lyapunov optimization method, the problem is further decomposed into the twin sub-problems of video layer selection and wireless resource allocation. Via solving these sub-problems, we derive a video layer selection strategy and a wireless resource allocation algorithm. Furthermore, we propose a beneficial routing policy for scalable video streams with the aid of the so-called segment routing technique in the context of SDN, which additionally exploits the collaboration of small-cell base stations. Our results demonstrate compelling performance improvements compared with the classic PID control theory-based method.

A cloud-fog scalable video streaming architecture for multimedia internet of things devices

Cloud computing can be used to provide guaranteed quality of video services. On the other hand, fog computing can lower the transmission costs by using collaborative machine-to-machine and near-user edge communications. In this paper, a cloud-fog scalable video streaming architecture for multimedia internet of things (IoT) devices is proposed, where fog nodes can collaborate with the rented cloud servers for efficiently delivering the requiring video frames to multimedia IoT devices. The proposed architecture strengthens the stability of the video streaming transmission and improves the quality of service (QoS) of multimedia IoT services with cloud-fog computing techniques. Simulation results show that the proposed cloud-fog scalable video streaming architecture can provide smooth video streaming services to multimedia IoT devices with low-cost than traditional video streaming approaches.

A cloud-fog scalable video streaming architecture for multimedia internet of things devices

A cloud-fog scalable video streaming architecture for multimedia internet of things devices

Energy-Efficient Traffic Rate Adaptation for Wireless Streaming Media Transmission

Energy-efficient streaming of real-time scalable video over wireless fading channels with imperfect channel state information is considered in this paper. A traffic rate adaptation (TRA) scheme is presented to minimize the transmission energy consumption while satisfying the quality of experience (QoE) requirement by dynamically selecting the delivered encoding layers of streaming media depending on the channel condition. A partially observable Markov decision process model is introduced to formulate the energy-efficient QoE-guaranteed TRA problem. Based on the Lagrange method of multipliers, an online policy iteration algorithm is proposed to solve the constrained optimization problem. This algorithm performs policy evaluation and improvement without intensive computations and prior knowledge of channel statistics. The adaptability to unknown environments and high computation efficiency make the proposed scheme attractive for implementation in real-life mobile systems. Simulation results demonstrate the effectiveness of the proposed method.

Dynamic Resource Allocation and Layer Selection for Scalable Video Streaming in Femtocell Networks: A Twin-Time-Scale Approach

Scalable video streaming over femtocell networks relying on two-tier spectrum-sharing is designed for coping with time-varying channel conditions, stringent video QoS requirements as well as with strong cross-tier interference between the over-sailing macro- and the femtocells. Dynamic video layer selection and resource allocation are invoked to enable the adaptation of the scalable video streaming service to the dynamics of both channel quality and interference price fluctuations. We formulate the design as a constrained stochastic optimization problem, which strikes a compelling compromise between the perceivable quality of experience and the monetary implications of the interference. Since the time scale of resource allocation is more short term than that of the video layer selection, we decompose the original long-term utility optimization problem into a pair of readily tractable subproblems with the aid of two different time-scales by invoking the powerful technique of Lyapunov drift and optimization. By exploiting the specific structure of these subproblems, low-complexity algorithms are derived for dynamic video layer selection and resource allocation, which rely on the near-instantaneously available information rather than on any prior statistical knowledge. Finally, we derive the analytical bounds of the theoretically achievable performance. Experimental results are presented for characterizing the performance attained.

Controllable Multicast for Adaptive Scalable Video Streaming in Software-Defined Networks

Scalable video coding is a promising technique to enable flexible video transmission for heterogeneous terminals and varying channel throughput. However, it is challenging to perform in-network adaptation in conventional networks because the network nodes are uncontrollable and transparent for media streaming applications. Software-defined networking (SDN) is an attractive network technology that supports the applications to collaborate with network nodes to achieve intelligent and dynamic service provisioning. Against this changing network landscape, we redesign the scalable multimedia multicast streaming by exploiting the complete network knowledge of the SDN controller to enable intelligent scalable video transmission. The proposed scalable multimedia multicast streaming framework is capable of in-network identifying, processing, and manipulating the media streams. In order to achieve the in-network adaptation, we apply equivalent bandwidth theory to estimate the affordable video layers that a link may accommodate, and apply finite-state machine to implement adaptive enhancement layer switching for multicast paths. In contrast to IP multicast, the proposed method is a controllable multicast scheme, which provides admission control in a multicast context, in-network adaptation, and supporting heterogeneous devices having different display capability. We further implement a prototype for illustrating the success of the proposed solution. The experimental results are also presented to show the effectiveness of the proposed equivalent bandwidth based adaptive enhancement layer switching algorithm.

Window‐based adaptive technique for real‐time streaming of scalable video over cognitive radio networks

Window‐based adaptive technique for real‐time streaming of scalable video over cognitive radio networks

Buffer management for scalable video streaming

In a video streaming scenario, to cope with bandwidth variations, buffer management and intelligent drop packets policies play a critical role in the final quality of the video received at the user side. In this context, we present a buffer management strategy implemented at the source of a video co

Design of Finite-Length Precoded EWF Codes for Scalable Video Streaming

Expanding window fountain (EWF) codes, which can provide unequal erasure protection property, are used as an efficient application-layer forward error correction solution for scalable multimedia data transmission over packet networks. Similar to Raptor codes, precoded EWF codes can provide linear coding complexity. However, only when the information length is large, the precoded EWF codes in previous literatures can achieve good performance. In this paper, we carefully investigate how to choose code rates of precodes and degree distributions of EWF codes for different information lengths. Our proposed precoded EWF coding scheme can achieve superior performance compared to the previous scheme for small and moderate information lengths. Simulation results for the scalable video coding extension of the H.264/AVC standard show that, compared with the previous scheme, our proposed scheme requires a smaller reception overhead to recover the base layer.

QoE driven cross-layer scheme for DASH-based scalable video transmission over LTE

Recently using Scalable Video Coding (SVC) in Dynamic Adaptive Streaming of over HTTP (DASH) has attracted more and more attention. In this paper, we present a Quality-of-Experience (QoE) driven Cross-Layer Scheme (QCLS) for DASH-based scalable video transmission over LTE. Specifically, assuming the priority-based extraction be exploited for bitstream adaption, we first propose a new continuous Rate-Distortion (RD) model for scalable video stream. Then to guarantee continuous playback, a two-level rate adaption algorithm is presented: a novel throughput-based algorithm is implemented for dynamic selection of segment bitrate on the DASH client side at the first level, and the second level applies the rate adaption by designing a suitable packet scheduling strategy at the Base Station. The packets of each segment with lower priority that are still left in the packet queues when their playback deadline is missed, would be considered as the ones that are beyond the actual transmission ability and discarded by the second-level rate adaption. Furthermore, in order to reasonably utilize the wireless resources in LTE (Long-Term Evolution) system, a cross-layer optimization problem that maximizes the total weighted received quality of the currently transmitted segments for all clients is formulated. In view of its high complexity of obtaining the optimal solution, we develop an approach of the suboptimal solution, which can determine a locally optimal transmission strategy in resource allocation as well as the corresponding Modulation and Coding Scheme. Accordingly, the transmission rate of each client can be obtained. Simulation results show that our proposed cross-layer scheme can provide better performance than the existing ones for DASH-based scalable video transmission over LTE.

User-Centric Visible Light Communications for Energy-Efficient Scalable Video Streaming

An energy-efficient indoor visible light communication (VLC) system relying on dynamic user-centric (UC) cluster formation is designed for scalable video streaming. Explicitly, the radically new UC cluster formation technique is based on an amorphous user-to-network association structure, which is ultimately the basis of our energy-efficient indoor VLC system. Furthermore, in order to optimize the system-level energy efficiency, our objective function is selected by jointly considering both the video quality and the power consumption. We then propose a three-tier dynamic-programming-based algorithm for user/layer-level adaptive modulation mode assignment, for access-point-level power allocation and for cluster-level energy efficiency optimization, respectively. Based on a scalable video coded sequence, our simulation results demonstrate the superior performance of our UC clusters compared to the conventional cell design in terms of its energy efficiency, throughput, as well as video quality in most of the scenarios considered.

Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks

Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than $\text{10}$ % quality degradation compared to the best achievable performance.