Bitrate Selection Research Articles

Design and Analysis of QoE-Aware Quality Adaptation for DASH

The dynamics of the application-layer-based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work, we provide a DASH quality adaptation algorithm, named SQUAD, that is specifically tailored to provide a high quality of experience (QoE). We review and provide new insights into the challenges for DASH rate estimation. We found that in addition to the ON-OFF behavior of DASH clients, there exists a discrepancy in the timescales that form the basis of the rate estimates across (i) different video segments and (ii) the rate control loops of DASH and Transmission Control Protocol (TCP). With these observations in mind, we design SQUAD aiming to maximize the average quality bitrate while minimizing the quality variations. We test our implementation of SQUAD together with a number of different quality adaptation algorithms under various conditions in the Global Environment for Networking Innovation testbed, as well as, in a series of measurements over the public Internet. Through a measurement study, we show that by sacrificing little to nothing in average quality bitrate, SQUAD can provide significantlygt ; better QoE in terms of quality switching and magnitude. In addition, we show that retransmission of higher-quality segments that were originally received in low-quality is feasible and improves the QoE.

Design and Performance Evaluation of Network-assisted Control Strategies for HTTP Adaptive Streaming

This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. For this purpose, a max-min fairness optimization problem is solved at runtime. We compare two approaches to actuate the optimal solution in an Software Defined Networking network: The first one allocates network bandwidth slices to video flows, and the second one guides video players in the video bitrate selection. We assess performance through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming

Multimedia streaming allows consumers to view multimedia content anywhere. However, quality of service is a major concern amid heightened levels of network traffic caused by increasing user demand. Accordingly, media streaming technology is adopting a new paradigm: adaptive HTTP streaming (AHS). AHS is widely used for real-time streaming content delivery in the Internet environment. In streaming, selection of appropriate bitrate is crucial for adapting media rate to network variations and client processing capabilities while ensuring optimal service for the consumer. We evaluate a proposed client-driven three-level optimized rate adaptation algorithm for adaptive HTTP media streaming. In the first stage, the algorithm chooses a suitable starting bitrate close to the available channel capacity. Next, the algorithm monitors the client parameters in real time, precisely detecting network variations and choosing a likely available bit representation for the next download segment. The algorithm controls and minimizes the effects of buffer stalls and overflow resulting from the brief network variations occurring between consecutive segments. The proposed algorithm is implemented in Dynamic Adaptive Streaming over HTTP (DASH) player and its performance compared to that of commercially available Gstreamer-based HTTP Live Streaming (HLS) and DASH players which use conventional segment fetch time–based adaptation and throughput-based adaptation algorithms respectively. This evaluation uses a real-time cloud server client and test bed streaming setup. The resulting analysis shows that the client-driven three-level rate adaptation (TLRA) approach allows adaptive streaming clients to maximize use of end-to-end network capacity, delivering an ideal user experience by precisely predicting network variations and rapidly adapting to available bandwidth, minimizing rebuffering events and bitrate level changes.

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.

Adaptive Bitrate Selection: A Survey

HTTP adaptive streaming (HAS) is the most recent attempt regarding video quality adaptation. It enables cheap and easy to implement streaming technology without the need for a dedicated infrastructure. By using a combination of TCP and HTTP it has the advantage of reusing all the existing technologies designed for ordinary web. Equally important is that HAS traffic passes through firewalls and works well when NAT is deployed. The rate adaptation controller of HAS, commonly called adaptive bitrate selection (ABR), is currently receiving a lot of attention from both industry and academia. However, most of the research efforts concentrate on a specific aspect or a particular methodology without considering the overall context. This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming. It starts by decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module. Each subcomponent encapsulates a particular function that is vital to the operation of an ABR scheme. A review of each of the subcomponents and how they interact with each other is presented. Furthermore, those external factors that are known to have a direct impact on the performance of an ABR module, such as content nature, CDN, and context, are discussed. In conclusion, this paper provides an extensive reference for further research in the field.

Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies.

Towards Smooth and High-Quality Bitrate Adaptation for HTTP Adaptive Streaming

Although HTTP adaptive streaming has been well documented for the cost-effective delivery of video streaming, it is still a great challenge to play back video smoothly with high quality under the fluctuating network conditions. In this paper, we proposed a novel bitrate adaptation algorithm for HTTP adaptive streaming. Our algorithm employed two approaches for throughput estimation and bitrate selection, which was evaluated on our testbed (a fully functional HTTP Live Streaming system) over a network, emulated using DummyNet. First, the throughput estimation method, based on the prediction of the difference between the estimated and instantaneous throughputs, was observed to respond smoothly to short-term fluctuations and rapidly to large fluctuations. Second, the bitrate selection algorithm, based on piecewise functions to define the variation range of the current bitrate, was found to result in smoother changes in quality with a higher average quality. The results of our experiments demonstrated the prospects of our bitrate adaptation algorithm for HTTP adaptive streaming.

IRate: Initial Video Bitrate Selection System for HTTP Streaming

Many HTTP streaming video systems have been developed and widely deployed in recent years. Previous efforts were mainly spent on improving the caching of videos or proposing mid-stream measurement methods to update the best bitrate. However, since the video length is often short, the mid-stream measurement may not even converge to the best bitrate due to insufficient bandwidth estimates. On the other hand, because of diversified Web infrastructure, estimating the actual network quality at the pre-stream stage is increasingly challenging for video service providers. In this paper, we propose IRate, which enables video service providers to proactively profile clients’ streaming performance by carrying out pre-stream measurement in the Content Delivery Network (CDN). With the measurement results, the video stream can start at the best video quality at the onset of streaming. This is especially beneficial to short video clips, which are very popular in the Internet today. IRate is composed of a probe kit and a quality oracle. The probe kit utilizes the pre-stream time window (e.g., user’s think time and pre-roll advertisement) for measuring network quality by running a lightweight measurement script on the Web page to induce probe packets from the IRate middlebox on the server side. With the measurement results, the quality oracle estimates the clients’ streaming performance by determining the highest initial bitrate with a pre-trained decision tree. Our testbed results show that IRate is able to achieve 80% accuracy in determining the bitrate within 10s. By having a better estimate of the best initial bitrate, the buffering time and rebuffering events are significantly reduced in HTTP streaming. Furthermore, the stability and the efficiency in dynamic adaptive streaming over HTTP streaming are also improved by about 40% and 36%, respectively. Our user quality of experience (QoE) experiment further validates that IRate can improve the QoE by more than 6% and the perceived quality of initial quality by 24% in the actual Internet environment.

Server Side Solutions For Web-Based Video

In contemporary video streaming systems based on HTTP protocol, video players at the client side are responsible for adjusting video quality to network conditions and user expectations. However, when multiple video clips are streamed simultaneously, an intricate application logic implemented in the video players overlays the TCP mechanism which is responsible for a balanced access to a shared network link. As a result, some video players may not obtain a fair share of network throughput and may be vulnerable to an unstable video bit-rate. Therefore, we propose to simplify the algorithms implemented in the video players, which are responsible for the adjustment of video quality and constrain their functionality only to sending feedback to a server about a state of the player buffer. The main logic of the system is shifted to the server, which is now responsible for bit-rate selection and prioritisation of the video streams transmitted to multiple clients. To verify our proposition, we performed several experiments in a laboratory environment which show that when the server cooperates with the clients, the video players experience fewer quality switches and the system achieves better fairness when allocating network throughput among the video players. However, this comes at the cost of worse utilisation of network bandwidth.

Bitrate allocation among multiple video streams to maximize profit in content delivery networks

Concomitant with rapid advancements in video streaming technology, concurrence video traffic has increased significantly on communication channels. Conflicts often arise among the various video streams on these communication channels when the available bandwidth is limited because the bitrates and transmission range required often vary. This study proposes a server-side-based rate allocation algorithm for content delivery networks (CDNs). Instead of simply considering bitrate selection from the perspective of network and client conditions, the algorithm combines user experience with video bitrate allocation to maximize viewer engagement. First, the values of users are evaluated and a user value computation method is proposed. Second, we developed a profit maximization bitrate allocation approach (PMBAA) that enables both content providers and CDNs to maximize profits by providing guaranteed video quality. At last, the results of test bed experiments and analyses verify that PMBAA enables high-value clients to obtain more preferable bitrates than the HTTP live streaming algorithm developed by Apple Inc.

A Greedy-Based Video Bitrate Selection Algorithm with Consideration of QoE Fairness for Adaptive Streaming over Software Defined Network

The video streaming may be the most important service of the internet nowadays, and it changes the viewing habits of the millions of people. The novel streaming technique - adaptive streaming helps the people to watch the videos smoother, that is, the video quality or bitrate would be changed with the bandwidth adaptively. However, the design of the traditional adaptive streaming does not take the entire internet into account, that is, each device may grab the bandwidth as much as possible, and the allocation of the bandwidth may not fairness which causes the unfairness of the quality of experience (QoE). In this study, the software defined network (SDN) is applied to be the centralized control for allocating the bandwidth. Besides, a greedy-based video bitrate selection algorithm is proposed for achieving the QoE fairness. By comparing the results to the brute-force method, the proposed method can achieve the QoE fairness as the optimum solution of the brute-force method.

Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive

Modern video players today rely on bit-rate adaptation in order to respond to changing network conditions. Past measurement studies have identified issues with today's commercial players when multiple bit-rate-adaptive players share a bottleneck link with respect to three metrics: fairness, efficiency, and stability. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited. In this paper, we present a principled understanding of bit-rate adaptation and analyze several commercial players through the lens of an abstract player model consisting of three main components: bandwidth estimation, bit-rate selection, and chunk scheduling. Using framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bit-rate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today's commercial players on all three key metrics across a range of experimental scenarios.

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.

Accelerometer-assisted 802.11 rate adaptation on mobile WiFi access

Abstract The expansion of 802.11 AP deployment provides opportunistic Wi-Fi access in underground mass rapid transit (MRT) system. However, such vehicular network faces the challenge of limited time for the MS on the train to connect the base station at the station. Therefore, to maximize the throughput within these several tens of seconds becomes crucial to the network. To achieve this goal, we propose accelerometer-assisted rate adaptation (AARA), a mechanism utilizes the out-of-band information of a train’s acceleration to improve the conventional rate adaptation scheme. AARA consists of two parts: First, AARA divides a train’s movement into four phases and performs real-time estimation on the train’s current movement phase. Second, AARA employs the estimation results to enhance the bit-rate selection during each phase. We conduct experiments on two different Taipei MRT systems: high-capacity MRT and medium-capacity MRT. The experimental results show that the average throughput of AARA outperforms that of the conventional scheme in different scenarios. In addition, we also design a mechanism of power saving with the aid of the movement estimation.

Channel adaptive video stream switching for broadband wireless links

This paper exploits an H.264/Advanced Video Coding codec's smooth stream switching to achieve robust video delivery across an IEEE 802.16 (WiMAX) broadband wireless link. As the wireless channel conditions vary over time, dynamic selection of bitrate and corresponding video quality will reduce the risk of harmful packet loss. The paper investigates choice of stream-switching with Secondary SP-frames or SI-frames relative to the selection of quantization parameter (QP) values. To control the switching points at the WiMAX server, the proposed scheme applies a feedback mechanism that monitors packet loss. As an additional suggestion, the paper considers an adaptive ARQ scheme to protect switching frames against packet loss. In summary, the broadband wireless streaming system gives more protection to higher quality video, reduces delay and packet loss, and improves received video quality. In particular, for the QP values selected, results show that increased quality primary-switching frames together with SI frames bring a significant gain in video quality compared with other switching schemes with secondary SP-frames. The other schemes in turn show an improvement to using `no switching' when streaming takes place over a typical WiMAX channel with burst errors. Link delay is also reduced.

MARS: Link-layer rate selection for multicast transmissions in wireless mesh networks

IEEE 802.11 devices dynamically choose among different modulation schemes and bit-rates for frame transmissions. This rate adaptation, however, is restricted only to unicast frames. Multicast (and broadcast) frames are constrained to use a fixed low bit-rate modulation, resulting in low throughput for multicast streams. Availability of high bandwidth and efficient use of the medium is crucial to support multimedia multicast streaming applications such as IPTV, especially in multihop mesh networks. To address this problem, we propose a rate adaptation algorithm for multicast transmissions in these networks. The proposed algorithm, MARS, is distributed in nature, and relies on local network measurements to select a transmission bit-rate for a given multicast group. The algorithm also facilitates the joint use of bit-rate selection and link-layer mechanisms such as acknowledgements and retransmissions to improve reliability of high throughput multicast streams. Based on implementation and evaluation on a testbed, the algorithm provides up to 600% gain in throughput compared to traditional 802.11 networks in some scenarios. Additionally, the algorithm can support multicast streams while consuming a small fraction (20%) of the resources compared to the basic 802.11 operation.

Performance of 802.11 b/g in the interference limited regime

Signal to interference plus noise ratio, SINR, is one of the main factors that affects the quality of wireless communication. While the impact of white Gaussian noise on a wireless channel is well understood, impact of interference remains one of the less explored areas. With the deployment of dense mesh networks, the interference will be a dominant factor that affects the transmission errors. This paper explores the performance of 802.11 b/g when subject to interference. The findings are based on various controlled experiments in the laboratory setting. One finding of this work is that in contrast to communication over links where the noise is Gaussian, in 802.11 b/g, the probability of successfully transmitting a packet is dominated by the ability of the receiver to synchronize with the carrier. As a result, changing to a lower bit-rate with same synchronization scheme will not make the transmission more resilient to interference. Moreover, we found that the impact of interference is significant even if the interference is 10 dB below the strength of the noise. The significance of this result on bit-rate selection is briefly explored.

QuickProbe

An accurate estimate of the available (or unused) bandwidth in a network path can be useful in many applications, including route selection in an overlay or multi-homed network, initial bit-rate selection for video streams or improving the slow start phase of existing TCP protocols. Previously proposed methods for estimating available bandwidth include Pathload [2], PTR [1], Spruce [4], and references therein. The Pathload technique has been shown to be reasonably accurate under a wide range of conditions by independent researchers [3,5]. PTR is more efficient and has been shown to have accuracy similar to Pathload in a more limited set of experiments [1]. With an accurate measure of the bottleneck link capacity, Spruce has been found to be more accurate than Pathload when a new cross traffic stream with known rate is injected. The principal drawback of these techniques is that they require on the order of hundreds of probe packets, and several to several tens of seconds to obtain the available bandwidth estimate. This paper develops a new bandwidth estimation technique, ‘QuickProbe’, that uses 19 probe packets to obtain a conservative estimate of the available bandwidth within a single round trip, and then uses 9-17 further probe packets in each subsequent round trip to refine the estimate. We have compared the QuickProbe and Pathload estimates for hundreds of Internet paths between PlanetLab and other nodes. The paths have measured round-trip times in the range of 20-800 milliseconds, capacities in the range of 0.1 600 Mb/s, and ratios of available bandwidth to capacity in the range of 5-95%. Over such paths, ‘QuickProbe’ obtains conservative available bandwidth estimates after two round trips that are a within a factor of 0.7 1.0 times the Pathload estimate.

Bit-rate detection circuit for rapidly reconfigurable rate-transparent optical networks

Optical networks which are rapidly reconfigurable and bit-rate transparent require agile clock and data regeneration (CDR) circuits that can respond quickly to different bit-rates. Real-time optical packet switching will also require burst-mode response. Present CDR techniques can be modified to rapidly synchronize to burst data if they are first tuned to the desired bit-rate. We propose and demonstrate a rapid-response bit-rate detection and selection circuit which can handle a discrete and continuous range of data rates from 10 Mb/s to 1.3 Gb/s. The circuit is suitable for programming conventional and burst mode CDR functions in regeneration, line termination, and real-time switching applications.

Effects of beam tilting on bit-rate selection in mobile multipath channel

With an increase in system bit-rate, unexpected distortion due to the multipath effects may be so serious that the system is rendered inoperative. The reduction of the multipath distortion by beam tilting at a basestation (BS) is theoretically analyzed using multipath propagation models; the system performance, characterized by the critical bit-rate and the probability that multipath distortion can be handled by the system, is evaluated. It is shown that the capability of the system to handle multipath distortion is improved, and the system bit-rate can be selected over a wider range with the introduction of beam tilting than that without beam tilting.