J. Cent. South Univ. (2013) 20: 2372−2377 DOI: 10.1007/s11771-013-1746-x

A segment version transcoding for client-centric wireless mobile media streaming

LEE Chong-deuk1, BANG Jun-ho2, JEONG Taeg-won1

1. Division of Electronic Engineering, Chonbuk National University, Jeonbuk 561−756, Korea;

2. Department of IT Applied System Engineering, Chonbuk National University, Jeonbuk 561−756, Korea

© Central South University Press and Springer-Verlag Berlin Heidelberg 2013

Abstract: An object segment similarity function is taken into account from the continuous media frames to measure the individual streaming profit of certain segment versions of a media object. Therefore, a new segment version-based transcoding (SVT) mechanism is derived for a quality of service (QoS) of client-centric media streaming in wireless mobile networks. The derived function utilizes the fuzzy similarity of certain segment versions of an object. This mechanism provides the effectiveness of reduction of the stream startup latency among segment versions, and the average access of each version. Thus, the proposed segment version transcoding mechanism reduces packet loss which in turn increases streaming performance and throughput. The performance of the partitioned segment versions is simulated and some segment versions are completed. The simulation results show that the proposed mechanism outperforms the other mechanisms in average cache hit ratio and in average startup latency ratio. Key words: similarity function; media object; segment version; client-centric media streaming

1 Introduction

In wireless mobile network environment, streaming media service is performed by connecting various mobile devices through the network. For the streaming media service, the management of huge amount of streaming media data is the key issue because of the limited resources especially limited resources and bandwidth of the wireless network [1−3]. For differentiated streaming of media content in the wireless mobile network application environment, the transcoding for the caching between media server and the proxy server should be considered [4].

Transcoding is the procedure used to change a coded signal into a signal of another code [5−6]. In streaming media service applications, transcoding means the technique that transforms compressed moving pictures into another specification of moving pictures or adjusts the bit rate, resolution and error resilience [7−9]. The proxy that performs transcoding is called the transcoding proxy. The typical transcoding proxy performs caching in addition to web proxy functions. The conventional web proxy, however, has the problem of deteriorating QoS in the process of streaming media objects due to delay, congestion, and interference [10−11].

CARDELI et al [12] proposed a coverage-based algorithm and a demand-based algorithm to solve this problem. The coverage-based algorithm caches the original version of objects, while the demand-based algorithm caches the transcoded version. Others have proposed the transcoding of web objects [3, 5, 12]. MOHAN et al [13] adapted web contents to mobile user profiles using the InfoPyramid data model. These algorithms, however, have problems of congestion and delay since they do not consider the size of media object, cache capacity of the proxy, similarity of media objects, segment version and sizes of media objects, bit rate, and frame rate.

Generally, transcoding a continuous media object is more of complex than transcoding a simple media object, because the streaming media have larger objects. Therefore, a different transcoding mechanism from that used for traditional web transcoding mechanisms must be considered.

This work presents a new segment version transcoding (SVT) mechanism using the fuzzy similarity to solve the problems of existing streaming media mechanisms. The SVT mechanism calculates the fuzzy similarity degree for partitioned segment versions of streaming media objects to construct a graph. SVT is used to specify the relationships between segments in determining the partitioned segment versions of media

Foundation item: Project(2011) financially supported by Research Funds of Chonbuk National University, Korea Received date: 2012−11−09; Accepted date: 2012−12−26 Corresponding author: BANG Jun-ho, PhD; Tel: +82−63−270−6098; E-mail: jhbang@jbnu.ac.kr

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objects. A fuzzy similarity relation specifies each segment version of media objects in SVT.

Thus, the proposed SVT mechanism aims to provide a ceaseless streaming media service by minimizing the QoS problem in wireless mobile networks. 2 Related work

Generally, users with excellent network service environment may enjoy high quality transmission service while users with poor network service environment may not. Thus, most proxy structures perform services with the bit rate unequal to the bandwidth for the streaming of media clip objects. Typical media proxy mechanisms do neither consider the streaming by partitioning segment versions of media objects nor perform services by integrating various versions of media objects [6,14−15]. CHANG and CHEN [2] proposed a mechanism which partitions different versions of objects during the caching. This mechanism is efficient for the segmentation of web objects. For media objects, however, this mechanism has restrictions due to the character of large size. QIN and ZIMMERMANN [15] proposed the selective caching mechanism to improve playback quality. This is an intermediate frame selection mechanism which caches frames according to the transmission rate. This mechanism, however, has the problem that cannot cache frames after the initial segment directly. MA et al [16] proposed a dynamic control video buffer mechanism and transmission scheduling mechanism for flexible playback. Generally, in terms of video reference, frames are classified as high usage frames and low usage. This kind of mechanism appears mainly in internet video such as live news.

CARDELI et al [12] proposed hierarchical caching structure for transcoding proxy. This mechanism distributes transcoding objects on hierarchical paths. This mechanism is efficient in the reduction of proxy load, while has the problem of high cost for transcoding due to the network latency. JIANG et al [4] proposed a transcoding mechanism using ORG (object relation graph). This mechanism has the problem of difficult proxy adaptation for clients with different environments.

KAO and LEE [3] proposed a function used to calculate the general object gained by using weighted transcoding graph. This mechanism performs transcoding by using weighting factor in streaming and performs cache replacement based on gain function. This mechanism, however, requires the information about which objects are used frequently.

PRANG et al [17] proposed three caching strategies for TEC (transcoding-enabled caching). This mechanism assumes that there are variants of the same video on the

network. First two algorithms of this mechanism select one of video objects and caches. This mechanism handles with different bit rates when the user requested. The third algorithm caches multi-versions of the same video objects to reduce the processing load for transcoding. These algorithms, however, have ambiguity in transcoding relation.

Conventional proxy caching mechanisms did not take into account continuous segment versions in the media object transcoding [18−20]. The transcoding delay, variety of media size, access frequency of the media object, and the reference number of segments affect QoS of the streaming. Streaming media objects are mainly cached in segments. Previous segment-based caching strategies cache segments with constant or exponentially increasing lengths and typically favor caching the beginning segments of media objects [21−22]. LEE and JEONG [7] proposed a fuzzy filtering-based segment management approach for caching large media streams. Blocks of a media stream received by a proxy server are grouped into variable-sized segments. The segment- based caching mechanism is very effective when the cache size is limited, when the media object size is constant, and when streaming requests are restrict. However, this mechanism has the drawbacks which are already known as size of media objects, the caching capacity, and the sequence of media object streaming.

The proposed mechanism enhances the performance of the streaming media service by minimizing problems due to uncertainty and ambiguity in transcoding of continuous media objects. 3 Segment version transcoding model 3.1 Preliminaries

Transcoding is a good policy to reduce startup latency and congestion occurring in streaming media service [2, 10, 23]. For example, in the transcoding of the higher bit rate media version x into the lower bit rate media version y, there occur uncertainty and ambiguity in transcoding. This work utilizes fuzzy similarity for each media segment to reduce streaming uncertainty occurring in transcoding and to provide differentiated services. Fuzzy similarity, defined by membership function μ, where μ is a number in [0, 1]. Each member in fuzzy similarity has membership value defined by μ [24]. (S) is the fuzzy set generated from the existing item set S. Each item of crisp set S has the membership value in [0, 1].

Definition 1: Fuzzy relation: The fuzzy set on the domain G×M has fuzzy relation with G and M.

Definition 2: Intersection of fuzzy sets: Fuzzy intersection of fuzzy sets A and B is A∩B, and μA∩B(x)= min{μA(x), μB(x)}.

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Definition 3: Union of fuzzy sets: Fuzzy union of A and B is A∪B, and )(xBA max{μA(x), μB(x)}.

Definition 4: Max-min composition of fuzzy sets: Let P(X, Y) be the fuzzy relation of X and Y and P(Y, Z) be the fuzzy relation of Y and Z. Then, max-min combination for P(X, Y) and Q(Y, Z) is μP·Q(x, z)=

XxYy maxmax {μP(x, y), μQ(y, z)} (1)

where max-min combination represents the relationship between sets X and Z.

Definition 5: For finite set X={x1, x2, …, xn}, fuzzy set A is defined by

)(/)(/)( 2211 nAAA xxxxxA

ii

n

iA xx /)(

1 (2)

where “+” means “OR” operation.

After fuzzy similarity analysis, fuzzy transcoding relation is constructed to specify transcoding of versions. Figure 1 was a directed graph with membership function μ with value in [0, 1].

Fig. 1 Directed graph with membership function μ

In Fig. 1, μs denotes the fuzzy similarity between

versions of object segments i. For each vertex vV[Gi], v is the version of transcodable object i. If there exists directed edge (u,v)E[Gi], version u for the object segment i is transcoded into version v. The cost for the trasnscoding of version u to version v, denoted by μR ׃u×v→[0, 1], is the fuzzy similarity form version u to version v.

In Fig. 1, the fuzzy similarities for edges from u to l and from v to l are denoted by μR(u, l)=0.8 and μR(l, v)=0.4, respectively.

Thus, the fuzzy similarity via the vertex l is μR(u, l) μR(l, v)=0.8 0.4=0.4. The fuzzy similarity from u to v is the maximum value of similarities for direct or indirect paths from u to v. 3.2 Segment similarity

Segment similarity is a process to optimize the transcoding by partitioning of media objects that exceed the capacity of the proxy cache. In general, the size of continuous media block objects is larger than that for

individual media segment frame. Thus, transcoding is performed to minimize the cache delay and the stream delay due to continuous and large media object block size. Since the priority is not given for object segments in continuous media block object and individual media segment frame, the streaming request for these objects causes longer delay and bottleneck. To solve the problem, this work performs fuzzy similarity relation for object segments. Fuzzy similarity relation is the fuzzy relation satisfying reflexive, symmetric, and transitive relation. Aα={x|μA(x)≥α} is applied to induce equivalence relation of Rα for fuzzy set A. Hence, the smaller α means the lower similarity and the larger α means the higher similarity. Media objects are arranged according to the similarity and transcoding priorities are determined.

As an example, let’s suppose a transcoding process by the fuzzy similarity matrix MR shown in Fig. 2, where MR is

MR=

12.08.03.02.06.0

2.012.02.08.02.0

8.02.016.02.06.0

6.02.06.012.01

2.08.02.02.012.0

6.02.06.09.02.01

Fig. 2 Transcoding process

In Fig. 2, α=0.2 means that the similarity relation is

very low. If α is larger than 0.8, the similarity relation of object versions is {v1, v3}, {v2, v5}, and {v4, v6}. The dotted line in the graph is α=0.8 and the bold line is α=0.9. This means that the transcoding of versions have higher relation. Thus, the transcoding of object versions becomes clearer with the increase of α−level [8, 10] or transcoding process of Fig. 2. 3.3 Version transcoding

The version transcoding process is the procedure to search object segments satisfying the media object block

v1 v2 v3 v4 v5 v6 v1

v2

v3

v4

v5

v6

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and the individual media segment frame. The same transcoding class is generated and managed for objects with high semantic similarity. In this work, the higher fuzzy similarity of an object segment is regarded as the more occurrence frequency and the more important object for the streaming media service. The relation between object segments is extended by the mapping of copy variable Ci,j, fuzzy matrix MR, and transcoding process. For this purpose, compatible α−level set is generated for mapped fuzzy matrix. Elements of the generated α−level set are object segments with very high similarity. Thus, the reduction of jitter delay and the improvement of search efficiency are acquired when clients requests a streaming media service. The algorithm for version transcoding is shown as follows.

Algorithm 1: Input: Continuous media block objects and

individual media segment frame Output: Object segments satisfying Aα={x|μA(x)≥α}

begin //Search of continuous media block objects and

individual media segment frames while (Bi≤m and k≥Sj) begin // Bi is the number of accessed media block objects. // m is the number of total media block objects. // Sj is the number of accessed object segments. // k is the number of total segments. While ((i, j)=!0) begin 1) Calculate fuzzy similarity of object versions. 2) Perform mapping for copy variable Ci,j, fuzzy

matrix MR, and transcoding process. end if ((Bi, Sj)==fuzzy similarity relation) then begin 3) Perform α−level. 4) Generate transcoding process graph. 5) Generate compatible α−level set. end

4 Simulation results

In the simulation, the total number of media objects is set to N, and 3 VoD sources are used as standard video sequences. Three VoD sources have frames of 350, 450 and 500, respectively. To simplify the simulation, the media object is assumed to have Pareto distribution and is limited to 5 MB in size. The other parameters are as follows: maximum bit rate is 1.28 Mb/s, packet size is 512 kb, link bandwidth is 10/100 Mb/s, and the average link bandwidth is 1.2 Mb/s. The simulation continued for 560 s with μ≥0.7, 0<α<1, and the time stamp of stream ts in [1, 20]. The mobile client is assumed to be connected to the IP backbone network using a wireless IP. We evaluated the performance of the proposed scheme by

using the simulation parameters shown in Table 1.

Table 1 Simulation parameters

Parameter Description

n Number of partitioned segments of object

Pos Partitioned segment of object

Li Length of i-th segment

μ Fuzzy value of i-th segment of object

λ Service response rate

Tcache Size of total cache

Fcache−1 Size of reserved storage to

cache first segment of object

Rrest-cacheSize of reserved storage to

cache rest segment of object

α Capacity of cache

β Size of reserved storage to cache rest segment

We evaluated performance with changing the cache

size, the number of media objects, and the value of α. Major metrics used in the evaluation are average startup latency, average cache hit ratio and average delay saving ratio. The proposed mechanism is compared and analyzed with the other existing methods of selective caching mechanism, hierarchical caching mechanism, weight-based transcoding mechanism, and the proposed SVT.

The first simulation analyzed the performance of the average cache hit ratio with increasing cache size and fuzzy similarity. As shown in Fig. 3, the proposed SVT achieves better performance than the others. The proposed mechanism shows average performance improvement compared with selective caching mechanism, hierarchical caching mechanism, and weight-based transcoding mechanism, respectively.

Fig. 3 Average cache hit ratio vs cache size

Figure 4 shows the average cache hit ratio

performing the media object block size from 1 MB to 4.5 MB. We simulated the performance with an increase of the fuzzy similarity and had better result than others

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Fig. 4 Average cache hit ratio vs fuzzy similarity

when the fuzzy similarities are 0.7, 0.8 and 0.9, respectively.

The second simulation analyzed the performance of the average startup latency with the changes of the cache size and the fuzzy similarity. Figure 5 shows the simulation result performing the media object block size from 1 MB to 4.5 MB when the fuzzy similarities (Fs) are 0.2, 0.4, 0.6, and 0.8, respectively.

As shown in Fig. 5, average startup latency shows the best performance when Fs is 0.8. Figure 6 shows the simulation result performing the average startup latency when the fuzzy similarities are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9, respectively.

As shown in Fig. 6, the proposed mechanism shows the increased performance with the increase of the fuzzy similarity.

In the third simulation, we analyzed the performance of the average delay saving ratio with increasing the cache capacity.

Figure 7 shows the simulation result of the average delay saving when the fuzzy similarities are 0.2, 0.4, 0.6 and 0.8, respectively. As shown in Fig. 7, average delay saving ratio shows the best performance when Fs is 0.8. Therefore, the proposed mechanism is not influenced by

Fig. 5 Average startup latency ratio vs cache size

Fig. 6 Average startup latency ratio with fuzzy similarity

Fig. 7 Average delay saving ratio vs fuzzy similarity

the size of media objects and the cache capacity. Hence, the proposed mechanism performs an efficient transcoding and streaming media services hold a stable state. 4 Conclusions

1) A new transcoding mechanism SVT is proposed for client-centric streaming media services in wireless mobile networks. The proposed SVT mechanism utilizes fuzzy similarity, which is used to determine the relationship of transcoding, for the object segment version of media objects. Therefore, SVT determines such QoS metrics parameters as delay saving, cache hit, and startup latency.

2) The performance of the proposed SVT mechanism is analyzed to confirm the simulation results. The simulation is performed with changing the cache capacity, size of media objects, and value of fuzzy similarity to evaluate such performance. According to the simulation results, the proposed SVT mechanism shows improvement in average performance compared with the selective caching mechanism, hierarchical caching

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(Edited by DENG Lü-xiang)