Performance evaluation of smoothing algorithms for transmittingprerecorded variable-bit-rate video

The transfer of prerecorded, compressed variable-bit-rate video requires multimedia services to support large fluctuations in bandwidth requirements on multiple time scales. Bandwidth smoothing techniques can reduce the burstiness of a variable-bit-rate stream by transmitting data at a series of fixed rates, simplifying the allocation of resources in video servers and the communication network. This paper compares the transmission schedules generated by the various smoothing algorithms, based on a collection of metrics that relate directly to the server, network, and client resources necessary for the transmission, transport, and playback of prerecorded video. Using MPEG-1 and MJPEG video data and a range of client buffer sizes, we investigate the interplay between the performance metrics and the smoothing algorithms. The results highlight the unique strengths and weaknesses of each bandwidth smoothing algorithm, as well as the characteristics of a diverse set of video clips     

Efficient schemes for broadcasting popular videos

We provide a formal framework for studying broadcasting schemes and design a family of schemes for broadcasting popular videos, the greedy disk-conserving broadcasting (GDB) family. We analyze the resource requirements for GDB, i.e., the number of server broadcast channels, the client storage space, and the client I/O bandwidth required by GDB. Our analysis shows that all of our proposed broadcasting schemes are within a small factor of the optimal scheme in terms of the server bandwidth requirement. Furthermore, GDB exhibits a tradeoff between any two of the three resources. We compare our scheme with a recently proposed broadcasting scheme, skyscraper broadcasting (SB). With GDB, we can reduce the client storage space by as much as 50% or the number of server channels by as much as 30% at the cost of a small additional increase in the amount of client I/O bandwidth. If we require the client I/O bandwidth of GDB to be identical to that of SB, GDB needs only 70% of the client storage space required by SB or one less server channel than SB does. In addition, we show that with small client I/O bandwidth, the resource requirements of GDB are close to the minimum achievable by any disk-conserving broadcasting scheme.     

Fast techniques for the optimal smoothing of stored video

Work-ahead smoothing is a technique whereby a server, transmitting stored compressed video to a client, utilizes client buffer space to reduce the rate variability of the transmitted stream. The technique requires the server to compute a schedule of transfer under the constraints that the client buffer neither overflows nor underflows. Recent work established an optimal off-line algorithm (which minimizes peak, variance and rate variability of the transmitted stream) under the assumptions of fixed client buffer size, known worst case network jitter, and strict playback of the client video. In this paper, we examine the practical considerations of heterogeneous and dynamically variable client buffer sizes, variable worst case network jitter estimates, and client interactivity. These conditions require on-line computation of the optimal transfer schedule. We focus on techniques for reducing on-line computation time. Specifically, (i) we present an algorithm for precomputing and storing the optimal schedules for all possible client buffer sizes in a compact manner; (ii) we show that it is theoretically possible to precompute and store compactly the optimal schedules for all possible estimates of worst case network jitter; (iii) in the context of playback resumption after client interactivity, we show convergence of the recomputed schedule with the original schedule, implying greatly reduced on-line computation time; and (iv) we propose and empirically evaluate an “approximation scheme” that produces a schedule close to optimal but takes much less computation time.     

Throughput optimization for video streaming proxy servers based on video staging

A video streaming proxy server needs to handle hundreds of simultaneous connections between media servers and clients. Inside, every video arrived at the server and delivered from it follows a specific arrival and delivery schedule. While arrival schedules compete for incoming network bandwidth, delivery schedules compete for outgoing network bandwidth. As a result, a proxy server has to provide sufficient buffer and disk cache for storage, together with memory space, disk space and disk bandwidth. In order to optimize the throughput, a proxy server has to govern the usage of these resources. In this paper, we first analyze the property of a traditional smoothing algorithm and a video staging algorithm. Then we develop, based on the smoothing algorithm, a video staging algorithm for video streaming proxy servers. This algorithm allows us to devise an arrival schedule based on the delivery schedule. Under this arrival and delivery schedule pair, we can achieve a better resource utilization rate gracefully between different parameter sets. It is also interesting to note that the usage of the resources such as network bandwidth, disk bandwidth and memory space becomes interchangeable. It provides the basis for inter-resource scheduling to further improve the throughput of a video streaming proxy server system.

Efficient Selective Frame Discard Algorithms for Stored Video Delivery Across Resource Constrained Networks

Video delivery from a server to a client across a network is an important component of many multimedia applications. While delivering a video stream across a resource constrained network, loss of frames may be unavoidable. Under such circumstances, it is desirable to find a server transmission schedule that can efficiently utilize the network resources while maximizing the perceived quality-of-service (QoS) at the client. To address this issue, in this paper we introduce the notion ofselective frame discard at the server and formulate the optimal selective frame discard problem using a QoS-based cost function. Given network bandwidth and client buffer constraints, we develop an O (N log N) algorithm to find the minimum number of frames that must be discarded in order to meet these constraints. The correctness of the algorithm is also formally established. We present a dynamic programming based algorithm for solving the problem of optimal selective frame discard. Since the computational complexity of the optimal algorithm is prohibitively high in general, we also develop several efficient heuristic algorithms for selective frame discard. These algorithms are evaluated using JPEG and MPEG video traces.     

EPCRTT-based smoothing and multiplexing of VBR video traffic

Applying video smoothing techniques to real-time video transmission can significantly reduce the peak rate and rate variability of compressed video streams. Moreover, statistical multiplexing of the smoothed traffic can substantially improve network utilization. In this paper we propose a new smoothing scheme, which exploits statistical multiplexing gain that can be obtained after smoothing of individual video streams. We present a new bandwidth allocation algorithm that allows for responsive interactivity. The local re-smoothing algorithm is carried out using an iterative process. In the proposed scheme the smoothed video streams are divided into fixed intervals and then a new transmission schedule for each interval is calculated. The problem of applying an optimal transmission schedule for aggregated smoothing video streams is shown to be NP-hard problem. Partitioning the whole stream into sections enables parallel processing of the smoothing algorithm in real-time before transmission. This approach allows partial transmission of the multiplexed stream while smoothing other intervals. The simulation results show a significant reduction in peak rate and rate variability of the aggregated stream, compared to the non-smoothing case. Therefore the proposed scheme allows us to increase the number of simultanusally-served video streams.

Server side,play buffer based quality control for adaptive media streaming

Existing media streaming protocols provide bandwidth adaptation features in order to deliver seamless video streams in an abrupt bandwidth shortage on the networks. For instance, popular HTTP streaming protocols such as HTTP Live Streaming (HLS) and MPEG-DASH are designed to select the most appropriate streaming quality based on client side bandwidth estimation. Unfortunately, controlling the quality at the client side means the effectiveness of the adaptive streaming is not controlled by service providers, and it harms the consistency in quality-of-service. In addition, recent studies show that selecting media quality based on bandwidth estimation may exhibit unstable behavior in certain network conditions. In this paper, we demonstrate that the drawbacks of existing protocols can be overcome with a server side, buffer based quality control scheme. Server side quality control solves the service quality problem by eliminating client assistance. Buffer based control scheme eliminates the side effects of bandwidth based stream selection. We achieve this without client assistance by designing a play buffer estimation algorithm. We prototyped the proposed scheme in our streaming service testbed which supports pre-transcoding and live-transcoding of the source media file. Our evaluation results show that the proposed quality control performs very well both in simulated and real environments.     

Wireless network interface energy consumption

With the proliferation of mobile streaming multimedia, available battery capacity constrains the end-user experience. Since streaming applications are expected to be long running, wireless network interface card's (WNIC) energy consumption is particularly an acute problem. In this work, we explore various mechanisms to conserve client WNIC energy consumption for popular streaming formats such as Microsoft Windows media, Real and Apple Quicktime. First, we investigate the WNIC energy consumption characteristics for these popular multimedia streaming formats under varying stream bandwidth and network loss rates. We show that even for a high bandwidth 2000 kbps stream, the WNIC unnecessarily spent over 56% of the time in idle state; illustrating the potential for significant energy savings.Based on these observations, we explore two mechanisms to conserve the client WNIC energy consumption. First we show the limitations of IEEE 802.11 power saving mode for multimedia streams. Without an understanding of the stream requirements, these scheduled rendezvous mechanisms do not offer any energy savings for multimedia streams over 56 kbps. We also develop history-based client-side strategies to reduce the energy consumed by transitioning the WNICs to a lower power consuming sleep state. We show that streams optimized for 28.8 kbps can save over 80% in energy consumption with 2% data loss. A high bandwidth stream (768 kbps) can still save 57% in energy consumption with less than 0.3% data loss. We also show that Real and Quicktime packets are harder to predict at the network level without understanding the packet semantics. As the amount of cross traffic generated by other clients that share the same wireless segment increases, the potential energy savings from our client side policies deteriorate further. Our work enables multimedia proxy and server developers to suitably customize the stream to lower client energy consumption.     

A composite algorithm for strength reduction and code movement optimization

In the presence of information regarding the execution frequencies of various control paths in a program, use of the tradeoff between code space and execution efficiency has been shown to aid in selective replication and optimal placement of code over a program region. This paper extends the scope of the code replication and placement strategies to optimization by reduction of operator strength. The resulting composite strength reduction-cum-code placement approach is shown to eliminate the latent suboptimalities of conventional strength reduction. The approach subsumes the conventional transformations of (1) common subexpression elimination, and (2) movement of loop invariant code, and is easily amenable to the use of efficient solution procedures.     

An enhanced client-centric approach for efficient video broadcast

Periodic broadcast is a cost-effective solution for large-scale distribution of popular videos. Regardless of the number of video requests, this strategy guarantees a constant worst service latency to all clients, making it possible to serve a large community with a minimal amount of broadcast bandwidth. Although many efficient periodic broadcast techniques have been proposed, most of them impose rigid requirements on client receiving bandwidth. They either demand clients to have the same bandwidth as the video server, or limit them to receive no more than two video streams at any one time. In our previous work, we addressed this problem with a Client-Centric Approach (CCA). This scheme takes into consideration both server broadcast bandwidth and client receiving bandwidth and allows clients to use all their receiving capability for prefetching broadcast data. As a result, given a fixed broadcast bandwidth, a shorter broadcast period can be achieved with an improved client communication capability. In this paper, we present an enhanced version of CCA to further leverage client bandwidth for more efficient video broadcast. The new scheme reduces the broadcast latency up to 50% as compared to CCA. We prove the correctness of this new technique and provide an analytical evaluation to show its performance advantage as compared with some existing techniques.

Online smoothness with dropping partial data based on advanced video coding stream

Most studies of smoothing video stream compute the required bit rate of video transmission to satisfy all the transmitted data. In this paper, our proposed online smoothing with tolerable data dropping algorithm can adjust the bit rate as smooth as possible. Several multimedia encoding schemes, such as advanced video coding (AVC), can support partial data dropping to adapt to available bandwidth network. The AVC stream can be adapted by smoothing algorithm to ensure video quality for a given set of constraints where these constraints may be either static after the session set up or may dynamically change over the session duration. Our algorithm is based on the online minimum variance bandwidth allocation algorithm to look ahead a window of frames, dynamically adjusting the required bit rate such that ensuring smoothness when the buffer encounters underflow or overflow for video stream. Furthermore, we add the scheme of data dropping into this algorithm to increase the possibility of smoothing bit rates. The experimental results show the peak rate, the average ratio of dropped data, and the coefficient of variation for five test sequences with different content characteristics such as the average frame size, the peak/mean ratio of frame size, and the average frame bit rate. Experimental parameters are varied by window sizes and tolerable dropping ratios. The algorithm can significantly reduce the peak rate and the coefficient of variation when the transmitted packets are allowed dropping by a user-defined dropping ratio.     

The Performance of greedy algorithms for the on-line steiner tree and related problems

We study the on-line Steiner tree problem on a general metric space. We show that the greedy on-line algorithm isO(log((d/z)s))-competitive, wheres is the number of regular nodes,d is the maximum metric distance between any two revealed nodes, andz is the optimal off-line cost. Our results refine the previous known bound [9] and show that AlgorithmSB of Bartalet al. [3] for the on-line file allocation problem isO(log logN)-competitive on anN-node hypercube or butterfly network. A lower bound of (log((d/z)s)) is shown to hold.We further consider the on-line generalized Steiner problem on a general metric space. We show that a class of lazy and greedy deterministic on-line algorithms areO(k· logk)-competitive and no on-line algorithm is better than (logk)-competitive, wherek is the number of distinct nodes that appear in the request sequence.For the on-line Steiner problem on a directed graph, it is shown that no deterministic on-line algorithm is better thans-competitive and the greedy on-line algorithm iss-competitive.A preliminary version of this paper has appeared in theProceedings of the Workshop on Algorithms and Data Structures, 1993, Montréal. The first author's research was partially supported by NSF Grant CCR-9009753, whilst that of the second author was partially supported by NSF Grant DDM-8909660 and a University Fellowship from the Graduate School, Yale University.     

P2P transfer of partial stream in multimedia multicast

In this paper, we propose a new multicast scheme that is based on the client-initiated-with-prefetching (CIWP) and peer-to-peer (P2P) transfer of a partial multimedia stream. In the CIWP scheme, when a new client joins an ongoing multicast channel, the server has to create an extra unicast channel to retransmit the partial stream that has already been transmitted. However, the unicast channel consumes some of the I/O bandwidth of the server, as well as some of the network resources between the server and the client's Internet Service Provider (ISP). To solve this problem, we propose the use of the P2P transfer algorithm to deliver the partial stream from a client that has already joined the ongoing multicast session to the newcomer. This P2P transfer between clients is limited to clients belonging to the same ISP. To further improve the performance, a threshold is used to control the P2P transfer. We performed analytical studies to show that the proposed multicast scheme can reduce the consumption of the network resources of the server, by utilizing the client's disk space. We also performed various simulation studies to demonstrate the performance improvement in terms of the use of the server's bandwidth and the waiting time for the clients’ requests.     

Reducing bandwidth requirement for delivering video over wide area networks with proxy server

Due to the high bandwidth requirement and rate variability of compressed video, delivering video across wide area networks (WANs) is a challenging issue. Proxy servers have been used to reduce network congestion and improve client access time on the Internet by caching passing data. We investigate ways to store or stage partial video in proxy servers to reduce the network bandwidth requirement over WAN. A client needs to access a portion of the video from a proxy server over a local area network (LAN) and the rest from a central server across a WAN. Therefore, client buffer requirement and video synchronization are to be considered. We study the tradeoffs between client buffer, storage requirement on the proxy server, and bandwidth requirement over WAN. Given a video delivery rate for the WAN, we propose several frame staging selection algorithms to determine the video frames to be stored in the proxy server. A scheme called chunk algorithm, which partitions a video into different segments (chunks of frames) with alternating chunks stored in the proxy server, is shown to offer the best tradeoff. We also investigate an efficient way to utilize client buffer when the combination of video streams from WAN and LAN is considered.     

On the Impact of Systematic Noise on the Evolutionary Optimization Performance—A Sphere Model Analysis

Quality evaluations in optimization processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyze the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between and . Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalized mutation strength. A residual localization error R can be quantified and a second to ratio is derived by minimizing R .     

Balanced Scheduling toward Loss-Free Packet Queuing and Delay Fairness

In current networks, packet losses can occur if routers do not provide sufficiently large buffers. This paper studies how many buffers should be provided in a router to eliminate packet losses. We assume a network router has m incoming queues, each corresponding to a single traffic stream, and must schedule at any time on-line from which queue to take the next packet to send out. To exclude packet losses with a small amount of buffers, the maximum queue length must be kept low over the entire scheduling period. We call this new on-line problem the balanced scheduling problem (BSP). By competitive analysis, we measure the power of on-line scheduling algorithms to prevent packet losses. We show that a simple greedy algorithm is (log m)-competitive which is asymptotically optimal, while Round-Robin scheduling is not better than m-competitive, as actually is any deterministic on-line algorithm for BSP. We also give a polynomial time algorithm for solving off-line BSP optimally. We also study another on-line balancing problem that tries to balance the delay among the m traffic streams.     

Optimal Coding of Multilayer and Multiversion Video Streams

Traditional video servers partially cope with heterogeneous client populations by maintaining a few versions of the same stream with different bit rates. More recent video servers leverage multilayer scalable coding techniques to customize the quality for individual clients. In both cases, heuristic, error-prone, techniques are currently used by administrators to determine either the rate of each stream version, or the granularity and rate of each layer in a multilayer scalable stream. In this paper, we propose an algorithm to determine the optimal rate and encoding granularity of each layer in a scalable video stream that maximizes a system-defined utility function for a given client distribution. The proposed algorithm can be used to compute the optimal rates of multiversion streams as well. Our algorithm is general in the sense that it can employ arbitrary utility functions for clients. We implement our algorithm and verify its optimality, and we show how various structuring of scalable video streams affect the client utilities. To demonstrate the generality of our algorithm, we consider three utility functions in our experiments. These utility functions model various aspects of streaming systems, including the effective rate received by clients, the mismatch between client bandwidth and received stream rate, and the client-perceived quality in terms of PSNR. We compare our algorithm against a heuristic algorithm that has been used before in the literature, and we show that our algorithm outperforms it in all cases.     

JINSIL: A middleware for presentation of composite multimedia objects in a distributed environment

In a distributed environment, the presentation of structured, composite multimedia information poses new challenges in dealing with variable bandwidth (BW) requirements and synchronization of media data objects. The detailed knowledge of BW requirements obtained by analyzing document structure can be used for efficient utilization of system resources. A client–server environment consists of various system components that are either dedicated to a client (e.g., client buffer space and BW) or shared across multiple clients (e.g., server buffer space and BW). A shared server could benefit from fine granularity advanced reservation of resources based on true BW requirements. Prefetching by utilizing advance knowledge of BW requirements can further improve resource utilization. The prefetch schedule needs also to be aware of the BW fragmentation in a partitioned server. In this paper, we describe the JINSIL middleware for retrieval of a composite document that takes into account the available BW and buffer resources and the nature of sharing in each component on delivery paths. It reshapes BW requirements, creates prefetch schedules for efficient resource utilization in clients and servers, and reserves necessary BW and buffer space. We also consider good choices for placement of prefetch buffers across client and server nodes.     

Constraint-based and SAT-based diagnosis of automotive configuration problems

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FP^NP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.     

Worst case analysis of a greedy algorithm for graph thickness

In this paper, we consider a greedy algorithm for thickness of graphs. The greedy algorithm we consider here takes a maximum planar subgraph away from the current graph in each iteration and repeats this process until the current graph has no edge. The greedy algorithm outputs the number of iterations which is an upper bound of thickness for an input graph G=(V,E). We show that the performance ratio of the greedy algorithm is .