Dynamic adaptive streaming over CCN: A caching and overhead analysis

This paper presents the usage of CCN, which is a candidate for the next-generation Internet, in combination with the new Dynamic Adaptive Streaming over HTTP (DASH) standard, which was recently ratified by ISO/IEC MPEG. In contrast to the Internet Protocol, which is mainly based on the host-to-host connection paradigm originated in the 1970s, Content Centric Networking (CCN) focuses on the content itself, instead of its location. Considering the dominance of multimedia traffic in todays' Internet, the streaming performance of DASH over CCN as well as the problems introduced by this combination is worth to be investigated in detail. Therefore, we evaluate the protocol overhead introduced by the usage of CCN compared to the HTTP versions 1.0 and 1.1. Furthermore, the performance of DASH over CCN under different network conditions is compared to the performance of HTTP 1.0/1.1. Our results showed that although CCN comes together with higher protocol overhead than HTTP 1.0/1.1 as well as a prototype implementation, it can definitely compete with HTTP 1.0 in media streaming. Based on the evaluation results, problems as well as improvement possibilities are identified, which are the basis for future work in this area.

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.

Recently, dynamic adaptive streaming over HTTP (DASH) has become an increasingly popular way to view video over the Internet. In particular compared to other video streaming services these DASH approaches deliver superior QoE to viewers. This is due to improved video segment selection. Generally, Transmission Control Protocol (TCP) CUBIC is the defacto transport layer protocol use by DASH. To improve the robustness of the transport layer to network congestion many other TCP variants were implemented such as Compound TCP and BBR. Nevertheless some were made to work specifically in LAN environments for example Agile-SD. However, recently another transport layer protocol User Datagram Protocol (UDP) has been used in Google’s QUIC implementation. To date no work has be found giving the performance of these transport layer protocols with DASH. In this paper we test the performance of Agile-SD, CUBIC, Compound TCP, BBR and QUIC using the BBA, MPC, Pensieve and Oboe DASH approaches. Experiments simulate congested bottleneck link conditions common at household routers where families view multiple videos at the same time. We observe Oboe and Agile-SD is the most promising combination with CUBIC and Pensieve next. However, even though QUIC was touted to have superior transport layer performance by Google it was the worst performing.

In the mobile video era, dynamic adaptive streaming over HTTP (DASH) is widely concerned due to its favorable performance in time-variant channel environments. Although DASH follows a decentralised and client-pull paradigm, networks are expected to play a more active role to assist rate adaption. On the other hand, heterogeneous cellular networks (HetNet) are considered as an efficient solution to cope with the exponential growth of mobile traffic, where cell association is one of the key issues. However, most of existing association methods were not designed for DASH services. In this paper, we jointly consider both the selection of multiple video versions for network- assisted DASH and the association strategy for cache- enabled HetNet. We aim to guarantee a premium quality of experience (QoE) with DASH over HetNet. An integer optimization problem is formulated and simplified into a multiple-choice knapsack sub-problem and a many-to- one matching sub-problem. A joint cell association and version selection approach is proposed through problem solving. In our scheme, users are associated with small cells and select different versions according to the required rates of video versions, channel conditions, cache states and the traffic loads. Simulation results show the effectiveness of our proposed method.

This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies on the TCP protocol for reliability in data delivery. TCP is known to drop its throughput performance by several fold in the presence of even 1% or 2% packet losses, which are common in wireless systems. This will force DASH to settle at a much lower video resolution, thus reducing the user's quality of experience. We show that the use of FRANC, an adaptive network coding protocol that provides both low delay and high throughput to upper layers, as a reliability mechanism for TCP can significantly increase video quality. As part of our analysis, we benchmark the performance of various TCP versions, including CUBIC, Reno, Veno, Vegas, and Westwood+, under different packet loss rates in wireless systems using a real testbed with Raspberry Pi devices. Our goal was to choose the most promising TCP version in terms of delay performance, in this case TCP Reno, and make a fair comparison between TCP running alone and using FRANC underneath for reliability. Our demonstrator with DASH in Raspberry Pi devices using the DASH benchmark, shows that the video rate delivered is 4× higher when using FRANC. Even in harsh packet loss conditions, FRANC is able to deliver higher data rates (increase 4×), while experiencing significantly shorter (decrease 10×) video lags.

Generative Adversarial Networks (GANs) have emerged as a powerful tool in the field of Dynamic Adaptive Streaming over HTTP (DASH) to enhance various aspects of video streaming. This paper presents a taxonomy that categorizes the applications and techniques of GANs in the context of DASH. The taxonomy covers several key dimensions, including video generation, compression, quality enhancement, bandwidth adaptation, dynamic bitrate streaming, and cross-modal applications. Within each dimension, specific subcategories are identified to capture the diverse applications of GANs in DASH. Additionally, evaluation metrics for assessing the quality and effectiveness of GAN-based approaches are discussed. The taxonomy serves as a comprehensive framework to understand and organize the different ways in which GANs can be utilized to improve the streaming experience in DASH. By providing an organized structure, this taxonomy facilitates better understanding, comparison, and exploration of GAN-based approaches in DASH and enables researchers and practitioners to identify areas for further research and development.

Dynamic Adaptive Streaming over HTTP (DASH) is referred to as a multimedia streaming standard to deliver high quality multimedia content over the Internet using conventional HTTP Web servers. As a fundamental feature, it enables automatic switching of quality levels according to network conditions, user requirements, and expectations. Currently, the proposed adaptation schemes for HTTP streaming mostly rely on throughput measurements and/or buffer-related metrics, such as buffer exhaustion and levels. In this paper, we propose to enhance the DASH adaptation logic by feeding it with additional information from our evaluation of the users' perception approximating the user-perceived quality of video playback. The proposed model aims at conveniently combining TCP-, buffer-, and media content-related metrics as well as user requirements and expectations to be used as an input for the DASH adaptation logic. Experiments have demonstrated that the chosen model enhances the capability of the adaptation logic to select the optimal video quality level. Finally, we integrated all our findings into a real DASH system with QoE monitoring capabilities.

Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard which adaptively streams video based on the link bandwidth between server and client. DASH encoded videos are chunked in small segments and each segment can have different representations. Switching between these representations enables adaptive streaming, which has the potential to reduce bandwidth consumption in cases where a video is not completely watched. In this paper, we present an analysis on the advantages and disadvantages of using DASH as YouTube's video streaming format. To perform this analysis, we make use of a YouTube video trace and analyze the potential reduction in bandwidth consumption by employing DASH in YouTube, based on user watching patterns. Results from our analysis show that by employing DASH with a segment interval of 2 seconds, we can obtain 95% reduction in bandwidth for low quality videos and up to 83% reduction for HD videos in cases where users do not watch videos completely, which is the case for ~ 42% of all video requests in our trace. Considering all videos requested in the trace the overall bandwidth reduction is 40% for low quality videos and 35% for HD videos.

Video services account for the largest share of all Internet traffic, demanding a network capable of supporting the requirements of delay-sensitive traffic. Fluctuations in network load can cause high delays in the queues of network routers, which tend to degrade the Quality of Service (QoS) for adaptive video streaming, such as Dynamic Adaptive Streaming over HTTP (DASH). This work is positioned in the scope of active management queues (AQM) to improve the QoS of a DASH service by means of dropping packets. One traditional AQM that adopts a packet drop policy is Random Early Detection (RED), developed to drain the flow in times of congestion and thus reduce queueing delay. We revisited and implemented a P4-based implementation of RED, named iRED (ingress RED), an algorithm capable of dropping packets at the ingress pipeline, an innovation compared to other AQM strategies based on dropping at the egress. iRED was evaluated in two scenarios. First, we compare iRED against state-of-art AQM algorithms employing egress packet dropping in terms of Round-Trip Time (RTT), throughput and their impact on resources usage. Our findings indicate that iRED outperforms existing P4-based approaches by approximately up to 2.5x in RTT and 0.75x in throughput for the given buffer sizes. Next, we compare iRED versus Tail Drop (TD) approach in an emulated programmable Content Delivery Network (CDN) employing DASH. Experiments indicate that the iRED improve the QoS by approximately 0.85x in terms of cached video available in the client’s buffer and 0.9x in Frames Per Second (FPS) played.

In this study, the authors focus on segment characteristic analysis and joint optimisation of modulation and coding scheme (MCS) selection, resource block (RB) assignment and the block error ratio (BLER) determination and segment adaptation scheme to satisfy dynamic adaptive streaming over HTTP (DASH) clients over wireless networks. First, the authors define a utility function as the continuous playback time that the packets scheduled with the allocated resource can support. Then, the authors formulate the MCS selection, RB assignment and BLER determination into a mathematical model. The relationship among the above three factors can be explored instead of performing MCS selection and RB assignment with the fixed BLER. By decomposing the original problem into some sub‐problems, the authors can get a solution to the original problem with low complexity. At the client level, the authors develop an adaptive segment request strategy based on the playback information, the segments' characteristics and the estimated transmission rate. To decrease the influence of the inaccurate estimations, an adaptive guard time interval based on the real playback information and transmission information of the previous segments is introduced. Simulation results show that the proposed algorithm can efficiently improve playback continuity and video quality over the existing algorithms.

In this work, we study the caching performance of Content Centric Networking (CCN), with special emphasis on the size of individual CCN router caches. Specifically, we consider several graph-related centrality metrics (e.g., betweenness, closeness, stress, graph, eccentricity and degree centralities) to allocate content store space heterogeneously across the CCN network, and contrast the performance to that of an homogeneous allocation.

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.

Currently, dynamic adaptive streaming over HTTP (DASH) standard is used to change the video resolution according to the network capacity of the end user’s. However, if the video resolution changes frequently, the attention of the users can be affected decreasing their quality-of-experience (QoE). In this context, this paper proposes a no-reference video quality metric that takes three impairment factors into account, initial buffering delay, temporal interruptions or pauses, and video resolution changes during a video transmission. Also, the temporal locations of pauses and resolution changes are considered. In order to perform this task, extensive subjective tests were conducted. Experimental results show that the evaluators’ QoE is highly correlated with the frequency of pauses, video resolution changes and initial delay. Based on these results a video streaming quality metric for DASH services (VsQM $_{\text {DASH}}$ ) is formulated and implemented on PC and mobile hand-held devices. Finally, the performance of VsQM $_{\text {DASH}}$ is validated with additional subjective tests reaching a person correlation coefficient of 0.99. Furthermore, experimental results demonstrated that the proposed metric has low complexity because it is based on application level parameters and its energy consumption is negligible in relation to the energy consumed by the video player; therefore, can be easily implemented in consumer electronic devices.

Nowadays, Dynamic Adaptive Streaming over HTTP (DASH) has become very popular in streaming multimedia contents. In DASH, a client estimates current network bandwidth and then determines an appropriate video quality with bitrate matching the estimated bandwidth. Thus, estimating accurately the available bandwidth is a significant premise in the quality of video streaming, especially when network traffic fluctuates substantially. To cope with this challenge, researchers have presented various filters to estimate network bandwidth adaptively. However, experiment results show that current schemes either adapt slowly to network changes or adapt fast but are very sensitive to delay jitter and produce sharply changed estimation. This paper presents a novel bandwidth estimation scheme based on Moving Average Convergence Divergence (MACD). We applied an MACD indicator and its two thresholds to classifying network states into stable state and agile state, based on the network state different filters are applied to estimate network bandwidth. In the paper, we studied the performance of various MACD indicators and the threshold values on bandwidth estimation. Then we used a DASH proxy-based environment to compare the performance of the presented scheme with current well-known schemes. The simulation results illustrate that the MACD-based bandwidth estimation scheme performs superior to existing schemes both in the speed of adaptively to network changes and in stability in bandwidth estimation.

There is increasing viewer interest and technological support for streaming immersive clips over the Internet. There are, however, challenges in supporting high quality of viewer experience, mostly due to the large amounts of the data associated with immersive video and spatial audio (Ambisonics). In situations where there are limited network resources, the streamed 360∘ content needs to be adjusted dynamically to meet the network constraints. Dynamic Adaptive Streaming over HTTP (DASH) adaptation is a key technology for delivering high-quality video over open radio access networks (RANs). DASH allows for efficient adaptation of video streams to the available network conditions. This paper introduces 360-ADAPT, a DASH-based adaptation solution on an Open-RAN architecture for increased quality remote 360∘ opera experiences. Unlike existing schemes, 360-ADAPT gives precedence to audio over the video when selecting bitrates, increasing the overall quality of the artistic act and improving use of resources and energy. The proposed 360-ADAPT was tested with real opera viewers in the context of an artistic-oriented platform for opera delivery, part of the Horizon2020 TRACTION project. Results indicate that 360-ADAPT achieves higher perceived quality levels than alternative solutions both in QoS and QoE metrics.