Optimizing User QoE through Overlay Routing, Bandwidth ...
1. Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding Maarten Wijnants , Wim Lamotte Hasselt University - Expertise Centre for Digital Media Bart De Vleeschauwer, Filip De Turck, Bart Dhoedt, Piet Demeester Ghent University – IBCN - Department of Information Technology Peter Lambert, Dieter Van de Walle, Jan De Cock, Stijn Notebaert, Rik Van de Walle Ghent University – MMLab - Department of Electronics and Information Systems Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding
Good afternoon everyone and thank you for the introduction. My name is Maarten Wijnants, I am a PhD student at the Expertise Centre for Digital Media, which is a research institute of Hasselt University, located in Diepenbeek, Belgium. The work I am presenting here today has been developed in collaboration with the IBCN and MMLab research groups of Ghent University, another Belgian university, and is titled “ Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding ”. As you can see, the title contains the term QoE, which is an abbreviation for Quality of Experience , which I will use throughout this presentation to more formally denote the usage experience provided to clients. Stated otherwise, it is a rather subjective measure of how satisfied a user is when using a certain networked application.
Here you can see an outline for this presentation. I am going to start with an introduction and motivate the need for QoE optimization in the current networking environment. Next I will present the network architecture we propose to perform end-to-end user QoE optimization and I will discuss its main constituting components. This will be followed by an evaluation of the proposed network architecture using representative experimental results. Finally, I will conclude this presentation by stating our conclusions.
In recent years, we have witnessed an expansive growth in the networked access of multimedia services. Compared to traditional services like web browsing and e-mail, these services impose much stricter requirements on the transportation network in terms of packet loss, congestion, delay and jitter . Furthermore, due to the recent popularization of mobile computing, service providers are increasingly targeting not only fixed but also mobile customers. Since fixed and mobile devices as well as networks have largely divergent capabilities , a highly heterogeneous usage environment is created, which in turn results in growing service dependability as well as adaptation requirements. Unfortunately, the current generation of networks is not always capable of guaranteeing that the requirements imposed by multimedia services are satisfied. For instance, the Internet only provides best-effort routing, meaning no guarantees are given regarding the throughput, packet loss and delay that will be experienced by network packets . The access part of a client's network connection can also cause severe problems, mainly due to its limited bandwidth capacity . In particular, compared to the core of the network, the so-called last mile is usually much less capacitated. As a result, insufficient last mile bandwidth may be available to support all services which a client is currently using. This will likely give rise to congestion and hence also an increase in packet loss and delay in case adequate techniques for the adaptation of network traffic are lacking .
Based on these observations , we believe current networks are often unable to provide users of multimedia services with an acceptable usage experience or, more formally, Quality of Experience (QoE). As a result, we believe there certainly exists the need for a network platform which supports full end-to-end user QoE optimization, and we have proposed one such architecture in our previous work. The contribution of this paper is that we introduce a novel transcoding service for our proposed network architecture which enables it to dynamically adapt the bit rate of video bitstreams that are encoded using the new and highly efficient H.264/AVC video codec. Furthermore, a secondary contribution of this paper is an illustration of how this novel transcoding service enables the networking architecture to optimize the QoE provided to the user to a higher extent.
The network architecture which we have proposed achieves full end-to-end user QoE optimization by employing a 2-tier approach. On the one hand, the proposed architecture enhances data dissemination in the network core by providing an overlay routing service which ensures resilience to possible issues/impairments like failing or congested core network links . On the other hand, the proposed platform also provides functionality to perform user QoE optimization on the last mile. More specifically, this is achieved through performing both last-mile network traffic shaping and multimedia service provision. By this latter, we refer to the possibility to apply processing on multimedia network flows before forwarding them over the access network. Implementation-wise , the proposed networking architecture consists of 3 types of components. First of all, there are the overlay servers and overlay access components. Combined, these 2 components provide resilient overlay routing. The third type of component are so-called Network Intelligence Proxy instances, which takes care of the last-mile QoE optimization. I will now briefly present some more details regarding these 3 types of constituting components before discussing the network architecture’s novel H.264/AVC video transcoding service.
First the overlay servers. These are deployed in the network core, for instance in several autonomous systems in the Internet . They maintain an overlay topology. More specifically, they perform active monitoring to obtain information on the connectivity between pairs of overlay servers, and this information is used to construct overlay routing tables. The Overlay Access Components on the other hand are located close to end-users and are responsible for determining whether traffic should be sent to the overlay servers. The rationale behind this is that it is not always necessary to use the overlay resources. More specifically, when there is no problem on the direct path between source and destination, the overlay network should not be used . The AC therefore monitors the quality of the direct IP connection. Only in case a problem with QoS is detected or if the AC notices that the connectivity to the destination is lost, it will send the packets to the nearest overlay server . The overlay server then exploits its overlay routing table to forward the traffic it receives from the access component to an access component that is located close to the target node. This latter access components transfers the traffic back from the overlay layer to the IP layer and subsequently forwards it to its final destination . This way, the connectivity between source and sink is maintained, even in case there are impairments in the network core.
Besides overlay routing components, the proposed network architecture also consists of Network Intelligence Proxy instances. Like the Overlay Access Components, these NIProxies are deployed close to the end-user and they aim to improve user qoe by intelligently managing content delivery to clients over the last mile of their network connection . As its name implies, the
iproxy attempts to accomplish this objective by incorporating different types of emph{awareness} or emph{context} in the transportation network. More specifically, the NIProxy is both network and application aware, meaning it (i) has knowledge of the state of the transportation network and, in particular, the currently prevailing access channel conditions and (ii) knowledge of the application(s) clients are currently running, which can vary significantly depending on the kind of application(s) under consideration. The importance or significance assigned by the user to different network flows (or types of flows, e.g. audio or video) is an example of knowledge which could contribute to the
iproxy's application awareness. Based on its dual contextual knowledge, the
iproxy performs last mile QoE optimization in two complementary ways. First of all , it provides network traffic shaping, meaning it is capable of orchestrating the last mile bandwidth consumption of networked applications. Generally speaking, based on its network awareness the
iproxy prevents over-encumbrance of the client's access network connection, which will normally result in optimal flow reception at destination since congestion and the resulting packet loss and delay on the last mile . while its application awareness is exploited to create an intelligent allocation of the downstream bandwidth that is actually available. The actual results that can hereby be attained largely depend on quantity as well as quality of the application-related information available to the NIProxy.
From an implementational point of view, the
iproxy's network traffic shaping mechanism operates by constructing and maintaining a emph{stream hierarchy}, a tree-like structure that is composed of both internal and leaf nodes. The internal hierarchy nodes implement a certain bandwidth distribution strategy, whereas the leaf nodes always correspond to an actual network flow. Different types of internal stream hierarchy nodes are available, each having distinct characteristics and capabilities. The WeightStream internal node, for instance, assigns a weight value to each of its child nodes and apportions bandwidth among its children according to the following formula. Unfortunately I do not have time to go into details, but as you can see, each child receives a portion of the bandwidth available to the WeighStream node as a function of both its current weight value w_i and the streams maximal BW consumption, denoted by MaxBW_i in the formula. As is the case for the internal nodes, there also exist different categories of hierarchy leaf nodes. In particular, both emph{discrete} and emph{continuous} leaf nodes are available. Discrete leaf nodes are limited to toggling the bandwidth consumption of their associated network flow between a discrete number of values. Their continuous counterparts on the other hand are capable of forwarding network flows to the client at any rate in the continuous interval [0, maximal stream bandwidth usage]. As a result, continuous hierarchy leaf nodes allow for a higher degree of flexibility in managing the client's downstream bandwidth, however at the expense of increased computational complexity since their operation often requires the adoption of time-consuming operations. The second QoE optimization technique provided by the NIProxy is multimedia service provision, meaning capable of applying processing on network flows containing multimedia content on behalf of its connected clients. As is the case for the network traffic shaping, services can query and exploit the
iproxy's dual awareness. The
iproxy's multimedia service provision functionality is implemented using a plug-in approach: each service corresponds to a
iproxy plug-in that can be (un)loaded dynamically as it becomes needed (obsolete).
On this slide you can see a schematic overview of the architecture of the overlay network which we propose in our paper. You can see the Overlay Servers and Overlay Access Components; the overlay servers are located in the network core, the ACs are deployed close to clients. Combined, these 2 components form an overlay layer, which is a simplified abstraction of the actual IP network layer. The overlay layer offers a resilient routing infrastructure to mitigate impairments in the network core (of zoiets). The network architecture also contains Network Intelligence Proxy instances, abbreviated as NIProxy, that are situated at the network edge and which take care of last-mile QoE optimization. The novel H.264/AVC video transcoding functionality of the network architecture, which I will discuss next, is implemented as a plug-in for the NIProxy.
The H.264/AVC video transcoding algorithm which we integrated in our networking architecture specifically targets on bit rate reduction, meaning it attempts to adapt the bitrate of encoded video flows without altering their resolution and framerate . The algorithm operates entirely in the compressed domain and as a result, only entropy decoding and encoding have to be performed. In order to lower the bit rate of the video stream, one or more transformed coefficients are set to zero based on a dynamically changing cut-off frequency. The transcoder is steered by a rate control algorithm which is responsible for ensuring the desired target bit rate is achieved. It does so by tracking the current buffer occupancy, based on which it estimates a bit budget for the current frame. For each frame, this target bit budget is approximated by dynamically adjusting the cut-off frequency. The transcoding algorithm with rate control was implemented and integrated in the network architecture as a plug-in for the
iproxy components. Consequently, these components are now able to dynamically set the desired bit rate of avc bitstreams on the last mile . In turn, this enables the management of avc flows in the client's stream hierarchy using continuous leaf nodes , since these nodes require an accurate enforcing of the bandwidth budget they calculate for their associated network stream. I will discuss the influence hereof on the user QoE next, through the presentation of representative experimental results.
The results I am going to present where produced on a testbed , whose physical layout is shown at the bottom of this slide . The testbed consists of 10 Linux PCs and contains three overlay servers, two nodes running both an overlay access component and a
iproxy instance , two multimedia clients and one streaming video server. Furthermore, the testbed also contains 2 Click impairment nodes which are capable of emulating varying network conditions. In particular, we used these nodes to artificially introduce random packet loss in the core network as well as to enforce bandwidth restrictions on the last mile. To evaluate the networking architecture, and the novel H.264/AVC video transcoding functionality in particular, communication sessions between the server and both multimedia clients were set up. However, the connection of only one of the clients was protected by the overlay and proxy components.
The experiment I will present here involved the simultaneous streaming of two avc video flows to both multimedia clients while their last mile network connection was artificially impaired in terms of available downstream bandwidth. The experiment itself can conceptually be divided in 5 consecutive intervals, where each interval transition was triggered by a certain state change. In particular, the switch from the first to the second interval was caused by the introduction of the second avc flow in the experiment, whereas the transitions from interval 2 to 3 and from interval 3 to 4 were initiated by shifts in avc stream importance. During the experiment, users could alter the importance/significance of video stream through the GUI of the client software. Finally, in the last interval, the last mile impairment was moderated so that the clients suddenly disposed of an increased amount of downstream bandwidth. A network trace, illustrating all avc network traffic received by the overlay-enhanced client during the experiment, is depicted on the right of this slide; the stream hierarchy which formed the basis for the
iproxy to calculate this bandwidth distribution is also shown. Due to the constrained nature of the experiment, the stream hierarchy consisted of only 3 nodes and had a very straightforward structure. In particular, the root of the hierarchy consisted of an internal node of type WeightStream, which was used to differentiate between the two avc flows involved in the experiment. Both these flows were represented by a continuous leaf node. The figure also illustrates the weight values that were associated with both leaf nodes during the different experiment intervals. In this case, the weight values were determined so that they directly reflected the relative importance of the involved avc flows, as specified by the user. As just explained, the avc transcoding plug-in was exploited to dynamically transcode the video streams to the target bit rates calculated by their associated continuous leaf node. Looking at the network trace, we see that during the initial interval only one avc flow was present and that there was no need for transcoding since sufficient last mile bandwidth was available to forward the flow to the client at its maximal quality. This situation changed with the introduction of the second avc flow in the second interval . Since both flows had identical weight values and comparable maximal bit rates, they were assigned a comparable bandwidth budget by the
iproxy's bandwidth distribution mechanism (i.e. approximately 62 KiloBytes per second (KBps)). At the beginning of the two subsequent intervals, the user incremented the importance value of the first avc flow. This resulted in this stream receiving an increased share of the available downstream bandwidth, at the expense of the second avc flow which now needed to be transcoded to a lower bit rate. Finally, despite its lower importance value , the additional last mile bandwidth that became available during the last interval was exploited to upgrade the quality of the second avc stream, since the first flow was already being forwarded to the client at its maximal bit rate.
A number of important findings can be deduced from the presented experimental results . A first observation is that the
iproxy, by leveraging its network awareness , ensured that the downstream capacity of the last mile was roughly respected throughout the entire experiment. Consequently, last mile congestion was avoided, resulting in a minimization of packet loss and delay and hence an optimal reception of the forwarded flows at client-side. Secondly, the produced bandwidth distribution correctly captured the relative importance of the involved avc streams (i.e. more bandwidth was assigned to more significant streams) . This result can be attributed to the
iproxy's application-related contextual knowledge. Finally, the availability of the avc transcoding service allowed the
iproxy to adapt video streams to a continuous range of bit rates , this way enabling the NIProxy to calculate more flexible and dynamic BW distributions and to achieve an optimal and full exploitation of the bandwidth available on the last mile. These findings did not apply for the non overlay-enhanced client. Since this client lacked management of its last mile downstream bandwidth and also could not profit from avc transcoding functionality, its user witnessed a usage experience that was far from optimal. In particular, the user did not dispose of any mechanisms to differentiate between network streams. In addition, a substantial amount of packet loss was detected due to the client's last mile connection being flooded with more avc data than it could transfer. This in turn resulted in decoding issues and hence a severely deteriorated video playback at client-side. To conclude, it is clear that both clients were presented with a significantly different QoE and as a result, the presented experimental results comprehensively demonstrate the QoE optimization capabilities of our proposed network architecture and, in particular, its novel H.264/AVC transcoding service. The paper also contains an experimental evaluation of the resilient overlay routing service provided by the proposed network architecture, but due to time limitations I will not present the results of this evaluation here.
I have presented a network platform that supports full end-to-end user QoE optimization. In particular, the platform employs a 2-tier approach and first of all provides a resilient overlay routing service to circumvent erratic parts of the network core like for instance failing or congested network core links . On the other hand, the platform also performs QoE optimization on the last mile of the network connection through the provision of client downstream bandwidth management and multimedia service provision. I have discussed how the proposed network architecture was extended with H.264/AVC video transcoding capabilities, which enables continuous H.264/AVC video adaptation. In other words, the novel transcoding functionality allows the platform to transform H.264/AVC video flows to an arbitrary bit rate. Finally, through the presentation of experimental results, I have demonstrated the positive impact of this addition on the QoE optimization capabilities of the proposed network architecture. In particular, it enables the platform to more fully and completely exploit the downstream bandwidth available to a client on its last mile and in addition allows for the creation of more dynamic and effective bandwidth distribution results. The outcome is an even further optimization of/improvement in the QoE provided to the user!
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